In this episode of Meet the Author, Anne Milley interviews world-renowned behavioral scientist Vic Strecher. A professor at the University of Michigan School of Public Health and Director for Innovation and Social Entrepreneurship, Vic shares with us the incredible physical and mental benefits of living a life of purpose, especially important in today’s COVID-19 era. Drawing from personal experience, his extensive research, and his book. Life on Purpose: How Living for What Matters Most Changes Everything, Vic also discusses how to set goals around the things that matter most in our lives and strategies for bringing full energy to fuel your purpose. A 30-year user of JMP, Vic concludes with commentary around the critical role statistical analysis plays in his work. ... View more

  In this episode of Meet the Author, Anne Milley interviews world-renowned DOE experts Doug Montgomery (Arizona State University) and Bradley Jones (JMP). Ten years in the making, their recently published book, Design of Experiments: A Modern Approach takes a new look at the powerful data analysis tool. Unlike many DOE textbooks, Montgomery and Jones’ doesn’t focus on the technical or mathematical parts of experimental design, but on the critical tasks of planning and designing your experiment. Every chapter starts with a “textbook design” experiment, included to show how important a thoughtful design is in conducting a successful experimental design effort. Though not the focus of the book, JMP is featured for many of the design and analysis discussed.     Anne Milley We are super excited to have to design of experiments rock stars with us today. Anne Milley We have Professor Doug Montgomery, who is the region's professor of industrial engineering and statistics at Arizona State University, and we have our own Brad Jones principal Research Fellow in the junk division of SAS. Welcome to you both. Brad Jones To be here. Doug Montgomery Thanks, thanks, nice to be here. Anne Milley We're excited about your new book, but let's start at the very beginning, what is design of experiments and how is it a unique approach to experimentation. Brad Jones Let me start out by saying what it isn't. It's not like the experiments that you saw, or did in a science lab where you're actually doing a demonstration of something that you already know. Instead, when we do designed experiments in industry, for instance. Brad Jones It's because we don't know the answer that we want to find and we want to learn something about how a system or a process behaves Brad Jones And so what we do is we do a number of tests where we change the inputs of the system or process in a very special structured way in order to find out how each of those inputs affects whatever output. We're interested in Anne Milley Perfect. Good. Well, please share with us the motivating factors that led you to co author your new book design of experiments and modern approach and Doug, let's start with you. Doug Montgomery Well, I've, I've been teaching Doug Montgomery For about 15 years Doug Montgomery At the university level. And I've also talked a lot of designers premise courses in companies and I I saw the, the technology in the field of experimental design changing and Doug Montgomery Brad and I talked about this quite a bit for quite a long time. And we felt that there was an opportunity for a for a different kind of textbook or a different kind of book about design. Doug Montgomery Many, many of you know that I am the author of it is Doug Montgomery In fact, it's in the 10th edition. Doug Montgomery And it's been pretty successful some something like 65% of the experimental design courses that are taught Doug Montgomery At the university level use my textbook, which is gratifying that means that there's a lot of instructors out there that like it. Doug Montgomery But what Brandon, I knew is that there were a lot of people. In fact, we found out that it's about 25% of the people that teach carnivals enforces that use know book at all. Doug Montgomery They, they work with slides and notes and God knows what. And, and I think this is a disservice to their students because I, I cannot imagine teaching a course like this with the amount of content, the content in it. Doug Montgomery Without a textbook that students can read study and master the details. So we felt that there was a need for a different kind of book. And so that's what started the planning for this and Doug Montgomery By the way, it took us seven years to complete this so so we we Doug Montgomery We put a lot of thought into into what we were doing and how we were going to go about doing that right. Is that about right, Brad. Brad Jones Yeah, I would. I thought I made tell a story about 10 years ago. You and I were at a we're giving talks at the joint statistics meetings. Brad Jones Right in Washington, DC. Brad Jones And the title of the session was, is there a new paradigm for design of experiments. And what I talked about at the very beginning of this was the various paradigms that design of experiments has gone through from our a fisher at the very beginning to modern software nowadays. Brad Jones And there's been some resistance over time to the idea of using software as your major way to create designed experiments. Brad Jones But one of the things that I thought was problematic. Back then 10 years ago was that there was no textbook that was built around the idea of using software as the way to get to generate designs. Brad Jones And so john saw, who's the co founder of SAS and and also started JUMP JUMP project really wanted a textbook that that felt that would fill that gap and so Brad Jones Now here we are 10 years later we finally have that book. Doug Montgomery It took a while. But we got it done. Anne Milley Yeah where's and we're so glad you did. Anne Milley So what are the unique features of this book and Doug, let's start with you again. Doug Montgomery Well, I think, I think there's a couple of things that are really Doug Montgomery Unique about the book is in Doug Montgomery Every, every chapter has an introductory example and in many cases the. The example is is a as a standard design what what Brad, sometimes called a textbook design. Doug Montgomery And then we show in many in most cases, how that how that textbook design is is actually an optimal design and you can create this with software and then we Doug Montgomery Start looking at, well, what, what if the problem was actually different, you know. Suppose suppose there were there were constraints or there were Doug Montgomery Some situation where that standard design didn't exactly fit and let me actually show how how straightforward. It is to create a design that that Doug Montgomery Deals with that situation very, very nicely. I think another unique thing in the book is that is that we don't spend a lot of time. Doug Montgomery On the details of statistical analysis. Doug Montgomery A lot of a lot of design books spend at least half of the book. Doug Montgomery Talking about the mechanics of the analysis of variance and presenting the you know the summation formulas and all sorts that we don't do that. We rely on the computer. Doug Montgomery To actually do the analysis because in practice. Nobody sits down with a calculator and does summation formula is to do an analysis of variance or or or if they if they're doing that they're misguided so Doug Montgomery So, so we try to get right straight to the to the to the use of the computer from from from the very beginning to constructing the design all the way through to the, to the end. Doug Montgomery Where you're analyzing the data and presenting results in a way that somebody that's interested in the problem can understand which is Anne Milley very streamlined, good bread, anything to add to that, about what makes the to the book. Brad Jones Well, it is unique that jump is throughout the book. Brad Jones The jump analyses and also the jump designs and at the end, there's an appendix that shows jump scripting commands that you can use to find out. Brad Jones quintiles of the normal distribution or the chi square distribution or the F distribution so that so that you don't have to have Brad Jones You know, all these indices at the end of the book that with all these tables of that are normally shown and textbook for design. And I think another thing that's that's good about this book is that oftentimes in textbooks, not yours. Doug, but Brad Jones Other textbooks. Brad Jones The design part is almost treated as if it's a given like the designs come down from God. And, you know, they're the only possible thing that you could actually do Brad Jones For this particular problem and and as a result it it makes a student think that there's something magical and mystical about the designs. Brad Jones And, you know, and that you must follow these very, you know, flexible design approaches in the future and that is that's totally the wrong thing to do. Anne Milley That's been the recipes are always the same. Brad Jones Exactly. Brad Jones Sometimes you have to change the recipe that may not have one of the ingredients that you need. Doug Montgomery Yeah, if the stuff you have in the refrigerator doesn't fit the recipe, you've got a problem. Anne Milley Exactly. Good. Good. Thank you both. I'm sure this is going to be so well received, for all the reasons you were just explaining Anne Milley Five years ago, Doug, you gave a really interesting plenary it jumped discovery summit where you talked about how design of experiments was practically declared dead 30 years ago. So what did these naysayers get wrong. And why is designed to experiment still so underutilized. Doug Montgomery Well, this, this is a really isn't a little interesting backstory to this. Doug Montgomery Around 1990 I collaborated with a couple of people from the university for john Cornell was one of them late john Cornell and Andre curry and we wanted to Doug Montgomery Yeah, either one of these NSF workshops that where we you basically invite all of the sort of presentations work. Doug Montgomery And it's sort of a window you invite lots of people Doug Montgomery In, you know, including the people who are going to be the speakers and leaders, so you get a pretty broad spectrum of people. So we run this proposal for this UNICEF. Doug Montgomery Workshop and these things are usually pretty automatic to get to get supported by NSF because it's one way they they build their research portfolio so anyway. Doug Montgomery The review came back and and they declined to fund the workshop and the principal reviewer said, well, why on earth do we want to Doug Montgomery Fund, an effort to see what research needs to be done in the field all the research that needs to be done has been done, the field is basically dead. Doug Montgomery And this this disturbed me a lot because I had a pretty active consulting practice in those days. Doug Montgomery And and i was out working with a lot of organizations and finding lots of applications of design and lots of problems that that that were Doug Montgomery unique and interesting and that hadn't really been explored very much and you know one of, one of the things that was that I was seeing just for instance was Doug Montgomery The emergence of split plot and in the industrial world that split plots have been around and hang experiments for years, but there was Doug Montgomery There was all this stuff going on in the industrial world who are split plotting was necessary, and the problem scenarios, they're Doug Montgomery Often different than they are in agriculture. And so that was that was lots of stuff that really needed to be addressed. And it was astounding to me that somebody who was an expert in the field could could think that all the interesting problems whir whir Doug Montgomery whir whir solved it was Doug Montgomery It was just Doug Montgomery I think design is still under utilized in the science and industrial world. Doug Montgomery I think a lot of it goes back to education. Doug Montgomery I think the way statistics is taught to scientists and engineers is is not as modern approach as as it could be. Doug Montgomery I used to tell my engineering colleagues, the way that we really prove this in engineering education is to have more funerals. Doug Montgomery We, you know, we basically need to have a lot of the whole garb that Doug Montgomery But then I think things will get better because, you know, for example, my Doug Montgomery experimental design course that I teach your Asus is a master's level course 500 level course. Doug Montgomery And I very often have well over 100 people in it and they're from all the different engineering disciplines and these people are getting PhDs and he Doug Montgomery And in material science and chemical engineering and they're, they're gonna go happen a lot. I'm going to become faculty or they're going to become Doug Montgomery Research engineers and research scientist in companies and they're there, they're going to want to use this material. They're going to want to use it. And so it's an it's an education. I think, I think it's an education thing. Anne Milley Yeah, I think there's also a Doug Montgomery big job. Doug Montgomery To do in terms of Doug Montgomery that haven't been exposed to somebody. It was just a big group of people out there that you know Doug Montgomery Middle. Middle level scientists and engineers people that went through the old approaches and well they think about running experiments they think about either the best guests or one factor at a time. And that's the way to do things, but Brad. I probably have some insight on that too. Anne Milley Yeah, that actually is a good segue into what I wanted to ask, Brad. Brad. So the book that you co authored Anne Milley With Peter cost optimal design of experiments has actually been used as a continuing education tool for many organizations. And I was curious if your new book with Doug. If you think that will also be used. Anne Milley In industry, as continuing education, maybe we can get some of this old guard to see things in a new way. Brad Jones Well, it's very possible that Brad Jones This this book that Doug and I have just done would be used again in industrial applications. Brad Jones I remember that. Brad Jones One of my friends from Eastman Chemical Company Kevin white bought his entire statistics team a copy of that book and then they took, he took turns teaching each other for from each chapter. So Brad Jones This book, could you be used in the same same way the there are 12 chapters and we can you could make each chapter be the subject of a of a one hour talk in a in a course, maybe a few day course and in modern statistics for experimentation. Anne Milley Okay. Very good. Thank you. Doug Montgomery And by the way, that the eat the book has got a PowerPoint slides that support every one of the chapters and so you know you using the book in the way that brand describes would be very straightforward for you. Anne Milley Oh good, that's useful to know and we have a URL to share with the audience at the end. Anne Milley But before the next question I want to read something. So back in 2012 200 was introducing you Brad at Discovery summit and I want to make sure I get right so I'm gonna read it. Anne Milley He said, as an old timer. Who's taught design of experiments for years and years and years. I want to testify to the fact that over the last several years. The art of experimental design has changed profoundly Anne Milley And if I had to teach courses. Now in the university or in industry, I would have to teach it in a profoundly different manner and a lot of that is due to Brad Jones. Anne Milley And I wanted to mention this because I think it relates to what you said earlier, Doug, about how statistics isn't taught well Anne Milley And it's been criticized for a long time, but I will. Thankfully, I will say there's evidence that it's changing, so that's good. Anne Milley But I wanted to more granular Lee go into, what do you think the rate of changes and how design of experiments is taught. Is it being taught more modern Lee, or do you see evidence of it changing as well. Anne Milley And I, you know, whoever wants to start actually dug, we'll start with you, you because you're still in academics. Doug Montgomery Well, I think, I think it is changing. Doug Montgomery I think, I think. Doug Montgomery More and more, you see courses on experimental design taught in in programs that are outside of statistics. Doug Montgomery For example, art, our Doug Montgomery Experiment design course here. And we actually have three courses that experimental design. We have the 500 level course we had a 600 level course and then Doug Montgomery PhD level course. And then we have a 500 level response surface course. It covers response surfaces and mixtures and a few other topics and Doug Montgomery More and more. Yeah, I think you're starting to see those courses taught outside of statistics their their their their top maybe taught like here, they're taught engineering. Here are our stats department does teach some work on experimental design, but Doug Montgomery And I don't want these people do good work. Okay, don't, I'm not saying that they're not doing the right but what they they teach is of course that's aimed at the geologists Doug Montgomery You know who the apologists are Doug Montgomery Psychologists the biologists. The sociologist, the what and the these folks need to do statistics. Doug Montgomery As as part of the research and so it's more of course on analyzing the data. Okay. And, and I won't even let my students go take the course because it's it's a big SAS template. Doug Montgomery They show them how to run pocket Nova for every, every conceivable situation on Earth. And because that's what those folks need to do you know and you know but but some of them come over and take and take our course. I get people from biology. Doug Montgomery I don't, I don't get people from from sociology or some of the other colleges, because of the math backwards. Anne Milley Okay. Doug Montgomery Yeah, they, they, we engineering we, you know, if you have, if you don't have a capitalist prerequisite. And of course, you know, it's, it can't be engineering. So Doug Montgomery So, so that's that's Doug Montgomery I but I do think by and large the education in experimental design. He's getting there. Anne Milley Well, and I think that experimentation is becoming more popular because more people are working with data and they're finding out that they're Anne Milley faster ways to learn from that data, but Brad. Did you want to add anything about your observations and how do E's being taught how that might be changing. Brad Jones Almost gratified to find out from our academic group and jump that there are over 150 different do eat courses already being taught using jump as the generator of the DMV and this textbook, then can be useful to all those courses because it makes it it makes Brad Jones Each section. Each new kind of design. Brad Jones Easy in a structured way, it introduces them from, from the very simplest designs to substantially more complex design like designs for nonlinear models. Brad Jones So we start, we start with one factor designs and two factor designs and then screening designs and so forth. And we also have Brad Jones Discussions of random blocks and split plots and so forth. So there's a lot of material in this book. Anne Milley Good. Well, and I just wanted to add on to that. So just even in your interactions with customers over the years. I think you've seen scientists and engineers, much more eagerly embrace monitor design of experiments because they see how it can really help them. Brad Jones Yes, I think that the semiconductor companies have really bought into modern design of experiments and even more so the pharmaceutical companies, the pharmaceutical applications have are complex and they require a lot of thought and and the old school designs just aren't up to it. Anne Milley Right there. Yeah. Doug Montgomery A lot of a lot of the consulting work that Doug Montgomery I've done has been with biotech firms and medical device companies. Doug Montgomery And these, these people are becoming and have become very big users. Doug Montgomery One of the things, and I discovered in in my consulting practices that people would would ask me to go. Could you come and teach a course. Doug Montgomery And so I would, but I would tell them that I'm only going to teach a course if if you have people that come to the course that actually have a project or a problem. Anne Milley Where they can use this Doug Montgomery And and so as part of the way I taught the course I would. I would spend like three days, teaching them a course and then I would come back. Doug Montgomery A couple of months later, maybe three months later. And we have another couple of days, there will be some again more new material, but I would have all these people talk about their projects. What have they done Doug Montgomery And most of them were very successful in using design. And what I found is it only took a few people to have a big success for the word to spread throughout these organizations and suddenly now everybody wants to do this. So, so, yeah, that was gratifying to see that. Anne Milley Right, we'd love it when good things go viral, good things. Anne Milley So the last question before we wrap up, Doug, I wanted to ask you, because I've heard you talk about some really fun and interesting examples of do we so if you have a favorite you want to share, that'd be great. Doug Montgomery Oh yeah, my, my all time favorite okay and and Doug Montgomery Any, any of you that know me, know that that I like to drink wine. Okay. And the reason I like to drink wine is I can't drink beer beer makes me sick. Doug Montgomery Physically I become part of the entertainment and so some years ago when I was at the University of Washington in Seattle. I was doing consulting work for a company down in Oregon. Doug Montgomery And the guy worked with. There was a really sharp chemical engineering guy and he can I got to be pretty good friends. And so we're out having Doug Montgomery drinks and dinner one night and and you know he says you know I've always wanted to be a winemaker. And I said, Well, what's stopping you. And he said well you know how to make a small fortune and wine. And I said, no, he said, you start with a large fortune and Doug Montgomery And he said, I don't have that. And they said, but but I am thinking about forming a partnership, because I've got this. I just bought this lovely Doug Montgomery Vineyard property in just outside of a little town called newburgh Oregon, and he said, I'd like to turn that into a producing vineyard and then start making wine. Doug Montgomery And I said, well, sign me up. I'll be a partner. And so he ended up putting 17 partners together and and so we had this property we trusted we planted and then we Doug Montgomery Sort of Doug Montgomery came to the realization that we didn't really know how to make one Doug Montgomery Which we could grow the grapes and we were going to sell the grapes to some folks but we Doug Montgomery Didn't know how to carry and then any said, Well, I'm a chemical in Doug Montgomery My first job as a chemical engineer. I figured out how to do it. And he said, Doug, you're going to help us that our company here. Doug Montgomery run experiments to solve some of the problems we have with our castings. Let's do that here. So we set out to develop a winemaking process using design experiments and we did fractional factorial experiments every year for five years. Doug Montgomery Wow and and we always started off with a long list of fat, we must have started off with 20 factors. Doug Montgomery We scoured the literature we we took all of the the Doug Montgomery You know the folklore from our friends in France that make Burgundy, we were going to make peanut war and we started running 16 one fractional factorial experiments because we had 16 little test barrels. Doug Montgomery That we had had made. And so Doug Montgomery We started doing this and I wouldn't let us run more than eight factors per year because eight factors 16 runs as a brand new resolution for so at least you can figure out the main effects. Doug Montgomery And so we did this every year for five years trying to figure out what the right Doug Montgomery Factors were and the last year we decided that, okay, we've got enough wine that we made this year. Doug Montgomery That we could have a commercial release so so we we decided to do that. And we also decided to enter the one that we had the one that we had made from our from our process. Doug Montgomery In a competition to see how it how it get Doug Montgomery And. And by the way, there's a there's a Doug Montgomery There's a secret to getting an award for your wine. Okay, here's the secret. You take your wine to the most obscure Doug Montgomery Read that county in your state. Okay. I have a generic name for this dog breath County. Doug Montgomery Okay. He grew up the dog breath COUNTY AND YOU ENTER THE LINE IN THE FAIR AND NOBODY KNOWS ANYTHING. Okay. And nobody makes anything it's any good. So if your stuff is at least drinkable. You're going to get the award. Doug Montgomery And trust me, you know, go to wineries, look at the awards on the wall, see where they're from. There are a lot of them from dog breath counting. So we went to the international wine competition okay in Indianapolis, Indiana 2000 lines, guess what we got a gold medal. Anne Milley Of course you did. Because excellent process. Doug Montgomery It was good stuff. Anne Milley I'm sure it was with Doug. We're out of time, but Doug Montgomery We built. We've built a successful business out of this. And yes, Anne Milley Yes, and Anne Milley Success. And yes, I've had the pleasure of sharing a glass of Anne Milley Wine with you. Thank you. Anne Milley Well, I want to thank you both. Brad, Doug. You guys are such innovators in this space. Thank you for all that you've done and for all the other innovation that you are enabling so thank you and Julian back to you.   ... View more

  Wharton Professor and best-selling author Jonah Berger has spent most of his career studying change, its impact on human behavior, and phenomena like viral marketing and social influence. In this episode of JMP On Air’s Meet the Author, Anne Milley interviews Dr. Berger to discuss his latest book, The Catalyst: How to change anyone's mind. Much like the idea of a catalyst in chemistry, Berger argues that there are a special set of circumstances one can apply to make change happen faster, easier and with less energy. The key is figuring out the barriers to change so you can lower the amount of energy an individual or group needs to employ to make a change. You’ll also learn the five barriers to change and more about the case studies and research Dr. Berger conducted to develop the core principles to overcoming objections to change he shares in his book … and this interview.   Full Transcript (Automatically Generated) So we are really excited to see your doctor Jonah Berger, as Julian said, He's a professor at Wharton. And he's an international best selling author. You can see some of his books here. And we're going to be featuring him as the first guest on meet the author, segment on jump on air. Jonah, thank you so much for taking the time to be with us today. We really appreciate it. Oh, thanks so much for having me. So we really enjoyed your book, contagious, which you spoke about at jumped discovery summit 2014. And we're very pleased to see your new book, the catalysts out and doing very well. Please tell us what inspired you to write it.   Yeah, so contagious came out a number of years ago. And since then, I've had the chance to work with a variety of companies and organizations. So everything from big companies like the Googles, and apples and Nikes of the world to small startups. And what I realized was that every company, every organization, every person I talked to basically had some version of the same problem, which is they all had something that they wanted to change it Employees wanted to change their boss's mind and leaders wanted to transform organizations marketing people wanted to change the customer's mind and change consumer behavior. Startups wanted to change industries, nonprofits wanted to change the world. But there was one problem, which is that change is really hard, right? Often people are pushing and providing more information and reasons and it wasn't working. And so I started in some sense on a journey trying to figure out what could there be a better way to change minds and drive action? And if so, what was it?   Well, what is a catalyst? And how does that concept apply to your approach?   Yeah, so many of your audience are probably familiar with the idea in chemistry of a catalyst. I think more of us are familiar with it. In the broader notion of sort of something that changes something else, but in chemistry, it has a very specific meaning that I think is actually quite useful one, which is, as you know, chemistry changes really hard. Not only is it harder and when to change our boss's mind, it's much harder to change. Certain molecules or compounds into different things. And so chemists often add substances to make change happen faster and easier. They often increase the temperature increase the pressure to make reactions happen. But there's a special substance that they use a special set of substances that don't require more temperature or more pressure. And sometimes they create an alternate way for reactions to occur. They make the same change happen faster and easier and with less energy. And you might say, well, how is that possible? But these things do it through lowering the barrier to change, they don't add more temperature, they don't add more pressure, essentially, they find an alternate path that requires less energy. And these substances are called catalysts. And that's exactly what the book is all about. Usually, when we try to change minds, when we try to drive action, we think about some version of pushing, we add more energy. We think if we just give someone more reasons, facts, figures, I just make one more presentation. People will come around, but that often doesn't work. When we push people they often don't go they often push And so the book is all about, hey, there's actually a better way, not by pushing. But by removing those barriers to change by figuring out what the obstacles or barriers are, and mitigating them. I think a really good analogy. If you think about it, you know, imagine a car and it's parked on like an incline or something like that, and you're trying to get it to go and you put your foot on the gas, and it won't go and it won't go. You know, often when we want to change something, we think we just need more gas, just step harder on that gas pedal people will move. But less often we say, Well, wait, maybe I just need to depress that parking brake. And so the book is really about, you know, what are those hidden parking brakes? those obstacles, those barriers that often get in the way of change without us realizing it? And how by meeting them, can we make change more likely,   so interesting, and reflect like the emotional attachment that people have? And you just need to remove those barriers to let them let go? Maybe?   And, sorry, emotional attachment is certainly one. You know, one thing I talked about in the book is this notion of endowment. We're attached to old things, you know, people who longer they've lived. In a home, for example, the more they value it above and beyond market price, because they're emotionally attached to it, right? I've lived there for so long all my memories are attached in this place. I don't want to let it go. And so there's lots of research on the status quo bias and the endowment effect that shows we're very reticent to get rid of old things. At the same time, we're also scared of new things, right? new things often involve uncertainty. So the variety of different things with both letting go of the old as well as being scared of the new and some other things as well that often make change hard.   Change is hard. Well, in the introduction to your book, you make the distinction between changing minds and changing behaviors. And I want to just read quickly, it was short a couple sentences. You write sometimes you don't need to change someone's mind to drive action. Sometimes people are already open to changing your behavior. You just need to remove the roadblocks that make it easier to happen, as you were just saying, but then I have to ask too. Did you get some flack for the subtitle of how to change anyone's   mind? You know, I spent a while thinking about what what to title this And I think the first thing when someone reads that subtitles ago, anyone, I mean, we can't change anyone's mind. Right? I mean, sure, maybe we can change some people's minds, but anyone? What about someone of a different political party? You know, what about someone who's really, you know, unwilling to change their minds. But what's interesting is, is you know, I interviewed some people for this book. And yes, they interviewed great leaders and great salespeople, and people got their boss to come around. But I also interviewed some people who change is even more difficult. I talked to hostage negotiators. They get people to come out with their hands up. I talked to substance abuse counselors, they get people to quit, even after they've tried dozens of times and failed. I even talked to a rabbi who got former grand Dragon of the Ku Klux Klan to renounce the Ku Klux Klan and, and sort of, you know, give up those affiliations. I talked to Republicans that became Democrats, I talked to democrats that became Republicans. And so what I don't mean to suggest is changing anyone's mind is easy. There are certainly some people that are harder to change and others there are certainly some issues that are harder to change than others. One distinction I make in the book is sort of between pebbles and boulders, right? Are we moving something that's relatively small? Like a pebble? Yeah, requires some some effort, but not that much effort. We're trying to move a boulder which is going to be a lot bigger and a lot harder to change. I'm sure some of us are trying to move big, difficult things. But even in those really difficult situations, even when we might say there's no way a democrat would become a publican, there's no way republican would become a Democrat. There's no way you know, Klu Klux Klan member would would give it all up. People did change their minds. And so I think if you apply these tools, right, if you understand those barriers that are preventing change, really can change anyone's mind.   Yes, and behavior to good. Well, so you mentioned barriers, and you mentioned one of them, but can you tell us the five barriers to change?   Sure. So the first is reactance. The second is endowment. The third is distance, the forces uncertainty, and the fifth is corroborating evidence. And if you put those five together, they are an acronym. They spell reduce, which is exactly what good catalysts do. They don't push harder. They don't provide more facts, more figures and more reasons. They figure out what those barriers are, and they mitigate them. And I think by the way, that's really hard for us to do. You know, I did a lot of research looking at a variety of different individuals, I find that barrier blindness is really prevalent. Many people, if you ask them what they want to change, they have a really good sense of the outcome that they want to achieve. But they have a very bad sense about the barriers that are preventing change from happening. You know, most people know the boss said no, or the client didn't accept the project or the thing didn't happen. But if you ask them why they're often unaware of why it didn't happen, and so really have to start with that person whose mind we're trying to change. Think about a doctor, for example, you know, you walk into a doctor's office, a doctor's and say, Okay, here's a band aid or here's a finger splint, they start with a diagnostic to figure out what the problem is. And the same thing here we have to start with that person who are trying to change start with them, figure out what things are preventing them from engaging that desired action and use To mitigate those barriers and encourage them to come around.   Very good. Well, you mentioned the different case studies very briefly, and they were just so compelling these examples to illustrate how you can overcome those five barriers. How did you curate these cases?   You know, I wish I could say I had a magic formula. And I know the exact right way to do it. You know, I teach at the Wharton School. I've been a professor there now for over 13 years. Examples came from class examples came from research examples came from presenting these ideas and executive education sessions. Often sometimes people would hear something and go, Oh, but you know, can really anyone's mind be changed, you know, you should talk to these type of people. And so it's really interesting seeing some of the same types of approaches across areas. The first chapter for example, is all about reactance. All about when you push people they often push back and I talked about some strategies. There were parenting experts talk about applying them with their kids and smart consultants talk about using them with their clients and really from two or three different angles, I saw different groups of people applying the same underlying idea in slightly different ways. And so part of the fun of writing this book was really seeing how the same basic psychological principles can be applied in a whole host of situations, sometimes applied slightly differently, sometimes with a bit more nuanced or a bit more detail in one place versus another. But the ideas, the core principles are very much the same.   I loved how you were so objective and how you treated, you know, these case studies to recount the ins and outs of how the change happened. But I'm wondering if people identify with one of those people like a democrat or republican or what have you think it's hard for them to read and examine their own ways of thinking,   you know, so, if the book came out, and there were some early reviews before it was actually even out so we gave it to some sort of readers who were able to read it online through the Amazon vine program, and a couple of those early readers got a Little bit upset, because they felt like some of the examples were things they didn't think needed to be changed. So you know, they might have been one member of a certain political party, they didn't believe someone should change to the other side, they might have felt a certain social issue, there was a right and wrong. And, you know, there's no reason that people should be changed on that social issue. You know, it's not my job to tell people how to be. I think that's a little bit narrow minded, but I think the principles are very much the same. And so I tried hard in the book, not to take a political stance, to show that, you know, whether you agree with one side or the other, there are people that move in both sides. And I think understanding those principles and why they work can be applied to any situation. I think some people would say, well, oh, you know, aren't we manipulating people? Or, oh, you know, aren't we using these tools for evil? I mean, that's, that's a tough question. And I think, you know, even a hammer can be used for bad things. It's not the hammer itself. That's bad. It's how we use it that that's bad. And so my hope really is to help people apply these tools for good to change the world in industry. ways by understanding why change hasn't happened already.   Right? Well, I like that you use such compelling examples I thought it was, it made it so much more interesting to read. Oh, thank you. I appreciate that. You mentioned earlier about, we shouldn't just push on the gas more, and we can't just, you know, keep going with the facts and the figures. So, why does that work? Or might that work with some people that are maybe a little bit more logical and don't have as many barriers to overcome?   Yeah, you know, I think particularly for this audience, more of a sort of a technical tech savvy, you know, highly educated audience. We think that people are like us, right, that everyone should behave rationally that if you just give people the right information, they'll come around. I think a good analogy to think about you know, imagine you're in a room with a chair and you want a chair to move in a certain direction. Pushing that chair is a great way to get it to go. Push that chair in the direction that you want it to go. The chair goes in the direction that you want it to go. But there's one problem. People are not chairs. And when you push people, they often dig in their heels, they don't just go along, they push back We have in some sense an anti persuasion radar defense system almost anything about a spidey sense or a missile defense system that goes off when we hear someone trying to persuade us, right, we avoid the message, we ignore it. Think about when you get a call from a telemarketer or an email from a salesperson, we often hit Delete without reading it. We know someone's trying to persuade us our defenses. Our radar goes up, we knocked down those incoming projectiles. But even worse, someone seems like they're listening. But they're actually spending a lot of time counter arguing. Yes, they're shaking their head and they're listening to what we're saying. But in their mind, they're thinking about all the reasons why what we're suggesting is wrong. Oh, that will cost too much. Oh, that won't actually work. Oh, that is difficult to to apply or this other person won't won't like it. And so rather than just sitting there passively listening, they're thinking about all the reasons why they don't like what we're suggesting, and the core that barriers reactance. I talked about that a lot in the reactance chapter, how people like to think they're the drivers of their own behavior. And so we really need to make them feel like they're They're in control. Rather than trying to sell something, get people to buy in. Rather than trying to persuade someone, get them to persuade themselves by understanding how to make them part of the decision making process and give them that sense of agency.   I think it's so important that we all know about different ways of thinking and overcoming change, because we all have different people that we deal with. So yeah, certainly. Yeah. Good, good. Well, um, does culture influence how people react to messages and recommendations, and warnings?   Yeah. It's an interesting question. I just wrote a piece for HBr related to the Coronavirus, all about sort of, you know, applying these tools and techniques to getting people to engage in health behaviors that they should engage in or avoid going out or wear masks and so on. And what's so interesting is often telling people not to do something actually can make them more likely to do it. Talk about that idea of reactance. When we tell people what to do, they say, well, don't tell me what to do. Do I want to do whatever I want? And so we need to figure out how to get them to come along, not by telling them what to do. But again, engaging them in that process. You know, you mentioned culture, I tell a great story in that reactance chapter about an anti smoking ad from Thailand. So it's this organization that wants people to call a quit line. Rather than telling people to quit. What they do is they have kids walk up to smokers on the street, and ask those smokers Hey, can I get a light? Now imagine you're a smoker, the little kid comes up to you and asks you to have a light you say no, of course not. Right? And they shoot these amazing videos of the smokers telling these kids why smoking is a terrible idea. bad for your lungs, you want to be able to exercise Don't you want to go run and play? Nobody knows more about the dangers of smoking than than smokers. And at the end of that interaction, the kid hands a smoker, a piece of paper that says you worry about me, but why not yourself, right? If you want to quit call this helpline. Now notice what they didn't do. They didn't tell us focus. You have to quit. Here's what you should do. They just did. What I call highlighting a gap, they pointed out a gap between what the smoker is doing for themselves and what they would recommend for someone else. And then they leave that gap to let the smoker deal with it. And it leverages what's called cognitive dissonance. When people's attitudes and their actions don't line up, they often want to do some work to bridge that gap to make their attitudes and their actions more in line. And so in this case, many smokers called the quit line and quit, because they didn't want to seem like they're being hypocritical. I told those kids not to smoke, I probably shouldn't smoke either. Now, that's examples from Thailand. But I'm sure many of us listening can resonate with that same idea. We can apply the same notion of highlighting a gap, very different context. But to the office, right? Imagine there's a boss or a colleague that doesn't want to kill an old project. The project is losing money or it's not working, but they don't want to kill it. They have that status quo bias. They're attached to it. Rather than telling them No, we should kill this project. What if you say, well, Hey, would you recommend another company to start this project or have a different department knew what we knew now? Would you recommend they do something similar and that You'd probably say no, I mean, knowing what we know now we definitely wouldn't start something like this. Okay, well then why are we doing it? Right? Again, not telling them don't do it, but encouraging them to see well hold on. That's an interesting point. I said this, but I'm also feeling this, these two things aren't in line. How do I make them work together and so encouraging them to do the work themselves?   Right, thank goodness people do such creative experiments for us to understand. Well, Jonah, this has been such a pleasure to have you thank you so much for taking the time and want to recommend your book again to our viewers. And if you want to get the very compelling introduction to Jonas both you can go to jump comm slash catalyst. Thanks again, Jonah ... View more

Sir David Spiegelhalter delivers the closing keynote for Discovery Online on "The Art of Statistics." Discovery Online’s closing plenary speaker, David Spiegelhalter, OBE, has many accolades, most notably Chair of the Winton Center for Risk & Evidence Communication, Centre for Mathematical Sciences at the University of Cambridge, and past president of the Royal Statistical Society. But he has also featured in some noteworthy documentaries for the BBC, is the reigning World Champion of Loop, and is host of the new podcast, Risky Talk, which debuted in March 2020. I had the honor of interviewing him for Analytically Speaking in 2019 after reading his most recent book, The Art of Statistics:   Learning from Data, which I thoroughly enjoyed. He had so many interesting things to share, one of which was rather surprising — his confession that the book is a textbook in disguise! He touched on this in his plenary for Discovery Online, saying that modern statistical courses should be motivated by “problem-solving, visual exploration of data, Bayesian methods” and more. In the introduction of The Art of Statistics, David writes, “improving data literacy means changing the way statistics is taught.” We are happy change is afoot on this front! David asks, “Are you a Data Detective?” We love that as SAS co-founder and JMP chief architect, John Sall often advocates being a data detective (to explore and find things out) versus acting as a lawyer (to prove or persuade). A slide from David Spiegelhalter's keynote talk at Discovery Online.   David drew on many examples in his most recent book, one of which was the suspicious number of Dr. Harold Shipman’s patients’ deaths. This is how his book starts, and for me, it was a bone-chilling page-turner.   What brilliant sleuthing to expose this mass murderer!   David points out that we often use numbers to persuade rather than inform. He says the best ways to communicate risks are by means of absolute risks and expected frequencies with carefully chosen graphics, as well as careful consideration about wording in press releases and media headlines.   I found that excellent advice as he concluded his talk with this guidance from philosopher Nora O’Neill: There are many more highlights worth sharing. David is so eloquent and engaging. I encourage you to watch the recording of his talk, along with other Discovery Summit talks on the JMP Community. And I highly recommend his book — if you registered for Discovery Summit Munich, you will be receiving it as part of the care package we are sending. You will surely enjoy reading it. ... View more

I had the pleasure of interviewing Sir David Spiegelhalter for a new episode of Analytically Speaking — fascinating! If you have seen any of his Understanding Uncertainty videos or other talks and interviews, then you know he shares his wisdom and insights with enthusiasm and wit. Here is a snippet of David talking about the importance of communicating uncertainty. His most recent book, The Art of Statistics:   Learning from Data, is excellent. The UK edition has been available for several months, and now the US published version is out. We talked about his interest in Bayesian methods, the concept of a microlife, communicating (and understanding) uncertainty, and many other things, but most of the conversation related to his book. Here are a few highlights (but you will still want to read the book!): Statistics is not a “bag of tricks.” It is not a mechanical activity. The way statistics is taught is changing, and it needs to change even more to take a broader view to incorporate creativity and judgment. Judgment comes into play very early to ascertain whether your problem can even be solved by data, let alone with the data you may already have. He includes work by Onora O’Neill, a philosopher of Kant and an authority on trust. He specifically discusses her checklist of whether trustworthiness is evident in assessing a claim based on data, meaning these claims need to be: Accessible Intelligible Assessable Useable Quantifying bias will become more important in the future (controversial, though it is). Watch the full interview for more interesting observations from Sir David  — and don't miss his most recent book! ... View more

Roger Hoerl says the problems to be solved in Industry 4.0 are too complex and diverse for any one method to suffice. Roger Hoerl, the Brate-Peschel Associate Professor of Statistics at Union College, gave a thought-provoking opening plenary talk at JMP Discovery Summit China in Shenzhen, the manufacturing capital of the world. With his 30-plus years’ experience in industry, much of that at General Electric, he has considerable expertise in statistical problem-solving and quality improvement for complex processes and problems. In this era of big data and growing complexity of processes, problems, and opportunities, statistical problem-solving and Industry 4.0 are of particular interest. Before considering what the improvement strategy for Industry 4.0 may be, Roger took the time to revisit the first three industrial revolutions — Industry 1.0, 2.0, and 3.0 — because each builds on the previous one. Given the complexity of problems to be solved in Industry 4.0, Roger says a portfolio approach is required. Large, complex problems can be addressed by drawing on statistical engineering. It's timely that the newly created International Statistical Engineering Association has been organized and will be co-located with the Spring European Network for Business and Industrial Statistics (ENBIS) meeting, where Roger will also be speaking. Here are some highlights from Roger's talk: In the last few industrial revolutions, Roger noted that data has been an enabler, but human creativity in approaches to improvement is also key. More data allows for the application of new analysis methods, which can be creatively applied in combinations (statistical engineering). Roger also highlighted the challenge to deal with complexity within organizations. To elevate the importance of improvement, organizations should consider a CIMO, Chief Improvement Officer, to take a holistic approach to improvement. The talk was very well-received and followed by lots of questions, many of which flowed into the break. You can see his slides here in the User Community. (Slides are in English.) For more from Roger, a longtime JMP user, you can check out The Integration of Big Data Analytics into a More Holistic Approach and his book, co-authored with Ron Snee, Statistical Thinking: Improving Business Performance. He has also helped create our free Statistical Thinking online course. ... View more

Having known fellow SAS colleague, Jin-Whan Jung, for more than two decades and watched his interesting and successful analytic journey progress, I was especially pleased to get to interview him for this episode of Analytically Speaking. He is an exceptional statistician with practical experiences in academia and industry. He now leads the growing Advanced Analytics Lab at SAS.   Jin-Whan Jung leads the Advanced Analytic Lab at SAS. Jin-Whan shares a great deal about his experience, but some of the most interesting bits (that he is allowed to discuss) are from his time as Vice President of Customer Relationship Management at Samsung Life Insurance in South Korea and from his current role, leading the Advanced Analytics Lab at SAS where he works with some amazingly creative and talented quantitative individuals.  A few highlights from the interview: When sharing how he and his team wanted to improve customer Lifetime Value (often referred to as LTV), the response from some fellow Samsung employees, was “LTV — is that the name of new Samsung TV?” Translating business problems into analytic problems and then translating them back from analytic solutions to business solutions is important.   He used an analytic triangle analogy to highlight the importance of quantitative creativity. A book on the power of reporting had a major impact on how he communicates. The core value of analytics transcends the ever-changing buzzwords (data mining, big data, data science…). Inspirational leaders make ordinary people be extraordinary people. Jin-Whan was also kind enough to do a version of the interview in Korean. Whichever version you watch, you will glean new insights and ideas you can use in your organization.  ... View more

Tim Harford describes four obstacles to trying something new. Several years ago when I first joined JMP, John Sall had just finished reading Tim Harford’s book, Adapt: Why Success Always Starts with Failure. He recommended it to me, and I’m so glad he did! Since then, I have enjoyed more of Tim’s works: Messy: The Power of Disorder to Transform our Lives, Fifty Inventions That Shaped the Modern Economy, and I subscribe to his Undercover Economist posts and have enjoyed several of his online talks (most recently, the TED Talk on the power of slow-motion multitasking). Because I also studied economics, I appreciate Tim’s thoughtful exploration of economic ideas, often where we don’t expect them. We were delighted to finally have him present the closing keynote talk for JMP Discovery Summit Europe. He has so many interesting topics and talks, and even though Adapt has been out for a while, we love the message of trying new things and adapting from what we learn to solve problems in our increasingly complex world. (Of course, we advocate thoughtful experimentation in all forms!) Tim has a gift for conveying his points through well-told stories, and his talk reminded me how much I enjoyed Adapt as he explored stories like Twyla Tharp’s risk-taking in the ballet/musical Movin’ Out and how we can make one bad decision after another as shown by an episode of Deal or No Deal?  He guided us through four obstacles or traps our minds can set for us, and how to avoid them so that we can achieve higher rates of success and learn from our missteps. The first is conformity. This was illustrated by an entertaining clip from a decades-old Candid Camera episode where unsuspecting participants in elevators succumbed to the behaviors of others in the elevator — facing the back of the elevator, removing their hats, and turning again to face the front of the elevator. To overcome conformity, we should add diversity, encourage debates, and in general, ask questions. The second and third obstacles are related: broken (or nonexistent) and slow feedback loops. We need feedback, and we should seek specific feedback as quickly as possible. The faster we get feedback, the sooner we can act on it and learn faster from our mistakes. The fourth obstacle is loss aversion, painfully illustrated by an episode of Deal or No Deal? To counter this obstacle, we need to think about the future, and we need to “make peace with our losses,” as the work of psychologists Daniel Kahneman and Amos Tversky instruct.  Tim nicely summarized the case for adapting: We all need to be observers of what it takes to adapt, and we need a willingness to risk failure if we truly want to succeed. You can view the entire talk in the JMP User Community.*  *Note: The recording of the talk will be available until March 14, 2020. ... View more

Maria Lanzerath, W.L. Gore's global statistics leader, advocates statistics, the scientific approach. Maria Lanzerath is making the world a better place. She has had a round-about path to becoming an industrial statistician, which makes her journey all the more interesting. Based in Germany, she leads the global statistics team at W.L. Gore. She shares a wealth of knowledge and best practices in this episode of Analytically Speaking, spanning a variety of topics: designed experiments, statistical process control, and analytical culture, to name a few. The decision-making culture at W.L. Gore sounds amazing.  Everyone’s areas of expertise are respected — scientists, engineers, statisticians — and they all contribute to knowledge-based decisions as a team. The team very thoroughly discusses the problem. They determine what they know, what they need to know, and then they know where they need to start experimenting to further their understanding. Maria points out that statistics is in W.L. Gore’s DNA. Bill Gore, the founder of the company, was a big advocate of statistics, and that perspective is reflected in the culture. Maria shares some amazing things she has done with designed experiments, with an especially nice example of a definitive screening design. She has turned design-of-experiment (DOE) skeptics into fans when they see its power and strategic value to save time and money AND to learn faster from data. Interestingly, she shares that DOE is mostly only used by American companies in Germany. Opportunity for more value! Maria relishes a good challenge — the more complex and complicated, the better, especially when her colleagues think a situation is hopeless. Her attitude is admirable when facing a good challenge. She is ever the optimist on the team to see what progress can be made. I love that she stresses the importance of creativity in statistics. She is an inspiration and clearly continues to enjoy a rewarding career in applied statistics. Maria is skilled at showing others (even those who don’t like statistics) how wonderful statistics is and what it can can do for them, although she considers herself the “one-eyed among the blind.” Watch the interview in this latest episode, and you will see how her advocacy of statistics and its application is making the world a better place. ... View more

Luna at one month My daughter has wanted a dog since she could say the word. She memorized an encyclopedia of dog breeds when she was five. First, she first wanted a Nova Scotia Duck-Tolling retriever, then it was a sheltie, and finally she settled on a Siberian Husky.  My husband and I both had family dogs growing up and wanted to have a dog, but we both travel for work. So we wanted our daughter to be older and more responsible to help (especially with a high-energy breed like huskies!). After saying no for years (we have other pets — cat, hedgehog, fish), we finally agreed and began to research and prepare for some months before finding the husky puppy our daughter selected, which she named Luna. Wanting to facilitate the housetraining, I insisted that everyone collect data on Luna’s business, noting the time, and being watchful for what signals she was sending to minimize accidents, which were also recorded. My daughter had researched that for each month a puppy ages, the puppy develops greater bladder control with the ability to hold it for about an hour longer. Eager for Luna to get to the point of sleeping through the night, I wanted to see the time lengthen between incidents; so all the data went into JMP.  I first showed this graph to my daughter using the free JMP Graph Builder App for the iPad. Further research revealed that puppies don’t have fully developed bladder muscles until 4-6 months, which is quite evident in this graph. (Luna was three months old when we got her, and soon after things stabilized, we stopped recording the data.)     The similarities between caring for a toddler and for a puppy were striking, which made me curious about developmental comparisons to humans. I found a website that lets you enter your dog’s breed and age in months to compute a rough equivalent in human years. Dogs develop more rapidly in the first few years, making the oft-cited 1:7 ratio for dog years relative to human years not really applicable. Since I have photos approximately corresponding to each month of Luna’s life, I thought it would be fun to show the progression using images as markers in Graph Builder in JMP. If we plot canine age relative to human age, it’s a simple linear relationship for the first year. To better show the more rapid rate of development early on, I include a plot showing dog age divided by human age.       And here is Luna’s age trajectory relative to human age for her expected life span.     Go over to JMP Public to find similar graphs and the data. Luna is almost 20 months now, so that's the equivalent of a 15-year-old. Given the breed and that my daughter is also 15, there is no shortage of attitude in our house! It helps that we all get plenty of exercise because we have to make sure Luna gets hers.  Always looking for ways to engage Luna’s brain, I was excited to learn about “nose work," or scent detection, in the book Being a Dog. It turns out that a beginner nose work class is offered where we completed Luna’s basic obedience training, and we signed her up. I don't know what opportunities there may be to collect data on her progress, but it will be fun to see how she does! ... View more

Susan Cain explains why it’s important to know where you are on the introvert-extrovert continuum. Are you an introvert, extrovert or ambivert? This is a question Susan Cain poses in the closing talk for JMP Discovery Summit, “ Quiet: How to Harness the Strengths of Introverts to Transform How We Work, Lead and Innovate ,” and it’s an important question to ponder. How we answer this question has implications not just for how we interact with others, but also how we interact with the world.   Susan shares research showing that introverts and extroverts are wired differently. Our nervous systems can be more or less responsive to stimuli depending on where we are on the spectrum of introversion to extroversion.  This has important implications for the best environments for us to work, think, create and thrive. Introverts prefer more quiet environments to be at their best. Susan’s own story about how she pushed herself to be more extrovert-like led her to think critically about her need for quiet. Thankfully, she shares more of this in her book, The Quiet Revolution , as well as in her talks.  To overcome our cultural bias in undervaluing traits of introverts and overvaluing traits of extroverts, she shares some ideas to make the most of our collective talents, regardless of where we fall on the introvert-extrovert spectrum. Here are just a few: Solitude and creativity go together; to foster creativity and innovation, allow time for solitude. Before meetings, introverts should brainstorm by themselves; this will yield more and better ideas than group brainstorming. Extroverts should schedule one-on-one time with introverts with advance notice to prep to “get the best of their brains.” We’re delighted Susan took the time to be with us for her talk and the book signing. She certainly has inspired how I think about the best way to develop leaders, manage groups and stimulate innovation. ... View more

Kevin White of Eastman Chemical knows the power of applied statistics. Statistical problem-solving at Eastman Chemical has a long history, and I was fortunate to hear about it from Kevin White, who leads the Applied Statistics Group at Eastman. Kevin is the featured guest on the latest episode of Analytically Speaking. The Applied Statistics Group continues to grow and be more strategically leveraged over the years because the group has repeatedly proven its value, solving a wide variety of problems and fostering innovation. Kevin’s father used to lead the group, so Kevin was raised hearing about the power of applied statistics at Eastman.   Kevin first encountered JMP as a student and has been using JMP software for more than 25 years. Even though he wasn’t exposed to newer statistical methods like optimal design of experiments (DOE) in school, he and his group make it a point to invest in learning new statistical methods on an ongoing basis. One way he fosters continuous learning is by encouraging his team to attend conferences like JMP Discovery Summit (in fact, Kevin is on the steering committee). Another way is assigning chapters from Peter Goos and Brad Jones’ Optimal Design of Experiments book for group discussion, followed by inviting Brad to speak to the group and field questions.  DOE drives many strategic decisions, enabling faster innovation, deeper process knowledge, optimized processes and differentiation of Eastman’s product value over competitor products. Kevin and his group make good use of many other methods: statistical process control, process screening, stability analysis, modelling, and more, describing JMP as their “Swiss army knife” in a recent customer success story. There is a strong data-based decision-making culture at Eastman. Kevin describes best practices that he says make their statisticians better, like the collaborative efforts that take place with scientists and statisticians. He notes that such efforts led to winning the Malcolm Baldridge National Quality Award. We hope you’ll watch this latest episode of Analytically Speaking to hear Kevin share more best practices and how analytics is a strategic asset at Eastman. ... View more

Julia O’Neill, Principal at Tunnell Consulting, says one of the most common problems that she helps clients overcome is the curse of dimensionality. Currently Principal at Tunnell Consulting with more than 30 years of experience using statistics and chemical engineering, Julia O’Neill uses statistical thinking to solve problems in biotech and pharma. Julia’s career path was influenced by the famous statisticians and authors Bill Hunter and George Box. She started out in chemical engineering but broadened her scope to include statistics initially through Bill Hunter’s class, Design of Experiments for Engineers, at the University of Wisconsin. In that class, she had a “lightbulb moment” when she thought, “This is always what I wanted to do but didn’t even know what they called it.” George Box used the term “bridging” when statisticians could help apply statistics in other fields. Julia has made great use of her niche bridging statistics and chemical engineering. I was so pleased to spend some time with her and chat with her on the most recent episode of Analytically Speaking. One of the most common problems that Julia helps clients overcome now is the curse of dimensionality. She is a fan of the ways JMP helps overcome this. In presenting results to stakeholders, she says the Screening platform in JMP can be an “incredible consensus builder.” Some of the scientists and engineers she consults with have ideas about what causes what, but their hypotheses usually aren’t all right. The Predictor Screening chart in JMP shows an ordered ranking, which Julia says lets them see all of their hypotheses, but in priority order. This helps them determine which factors are important; if their hypotheses are near the bottom of the ranking, it’s time to rethink things. Julia also shared some of the important work she oversaw investigating complex issues in vaccine manufacturing while at Merck. Her efforts yielded tighter control in manufacturing consistency and improved process robustness. This work led to similar statistical thinking to set up continued process verification for a cancer treatment that Merck was rolling out. It's clear she has a passion for helping pharmaceutical companies get critical new treatments to market quickly, especially for patients with so few options. Some statisticians at the Food and Drug Administration (FDA) invited Julia to give a course on applications of statistics in chemistry, manufacturing, and controls (CMC). As an advocate for greater use of statistics throughout development and manufacturing, teaching this course has inspired her to write a book to reach more scientists, engineers, and statisticians who want to get more information on applying statistics within CMC. I look forward to spreading the word about it! Julia talks about the power of design of experiments, quality by design, Six Sigma efforts, and more in our conversation. She has so many fascinating stories to share. Watch my interview in this latest episode, and you'll benefit from her extensive experience and expertise bridging statistics and chemical engineering. ... View more

“Design of experiments is by far the best tool to obtain process understanding," says Per Vase, Managing Partner at NNE A few months ago, I had the pleasure of spending some time with a longtime JMP user, Per Vase of NNE , an international company focused on pharma engineering and a JMP partner. Our conversation centered on making data-based decisions in the context of understanding your development and production processes — areas where Per has vast expertise. Per says predicting your development and production processes is essential for achieving consistency and quality, as well as for avoiding compliance issues (you could lose your license). It’s importance to know what results to expect and why you can expect them. Per also points out that things will change over time, and by better understanding your processes, you can then adapt to differences you can’t avoid. Per sees how processes can interconnect. He has experienced situations where the production quality issues were a result of product and process development issues. Addressing process improvements upstream leads to overall process improvement. We also discussed some interesting data challenges. Even for companies that are collecting production data, if they’re not using it, data quality can be very low (no eyeballs on the data at all can leave a lot unnoticed and unfixed). Per has witnessed many pharmaceutical companies wanting to see stability in their production data, so sometimes there is no variation in the data. But this lack of variation impedes a better understanding of production processes. Doing designed experiments makes the variation more evident, enabling companies to truly learn from the data. As Per so eloquently states, “If you want to see the influence of a variable, it has to vary.” This is why Per and his colleagues at NNE are such strong proponents of designed experiments. “Design of experiments is by far the best tool to obtain process understanding,” he says. With the new Functional Data Explorer in JMP 14, Per talks about some of the many opportunities they are pursuing in biotech with their process data over time, as well as optimizing their legacy products in production. These are a few of the highlights from our conversation. For more of Per’s wisdom, check out the latest episode of Analytically Speaking .  ... View more

Ron Kenett, Chairman, KPA Group How amazing to count Sir David Cox as one of your mentors, attributing him for the reason you pursued a degree in statistics and continuing to serve as your role model; to be fortunate to have known quality guru, Joseph M. Juran; and soon to be the recipient of the George Box Medal for outstanding contributions to industrial statistics. These are just a few fun facts about Ron Kenett. A prolific writer and researcher who also collaborates with many scientists, engineers, and other researchers on far-ranging applications in healthcare, pharmaceuticals, software and system testing, conditioned-based maintenance, information quality, and many others, we are delighted to have Ron as our featured guest on this month’s episode of Analytically Speaking .  Ron is chairman of KPA Group , an analytics consultancy which we are pleased to have as a JMP partner.  He is also a Senior Research Fellow at the Samuel Neaman Institute in the Center for Industrial Excellence, Visiting Professor at the Hebrew University Institute for Drug Research , and Visiting Professor at the Faculty of Economics, University of Ljubljana , Slovenia. He is very involved in the global statistical community and is a Past President of the Israel Statistical Association as well as the European Network for Business and Industrial Statistics (ENBIS). We are happy he made time for us while recently in the US. Given his many publications, we thought it would be fun to look at the titles using JMP Text Explorer. We discussed some of the many things Ron has written about on aspects of quality: quality control, quality improvement, management quality, service quality, Quality by Design (for which he did a guest blog post series, The QbD Column , on the JMP Blog), a framework for information quality, and more. Here's the text analysis in JMP:     Interestingly in this era of data, Ron views the statisticians’ role as the one to ensure “quality of information,” taking a lifecycle view of analytics to achieve this—from design, to data, to analysis, to results, and to reiterating the cycle. This is central to a book he co-authored with Galit Shmueli (a previous guest on Analytically Speaking), Information Quality: The Potential of Data and Analytics to Generate Knowledge.  Ron has authored, co-authored and edited many books . We discussed topics from several of them.  In addition to the book on information quality, we also touched on Analytic Methods in Systems and Software Testing , Multivariate Quality Control: Theory and Applications , and some topics from a new book he co-authored with Tom Redman which will be coming out in early 2019, The Real Work of Data Science: How to turn data into information, better decisions, and stronger organizations.    While Ron is able to reflect over a long, successful career, he also sees new opportunities for analytics where statisticians can play a key role. For example, Ron has been looking at the role of analytics in Industry 4.0 and shared some thoughts on this increasingly data-intensive topic. Ever active in keeping up with current trends and topics in analytics, Ron continues to accrue awards and accolades. For those of you who may be attending ENBIS in Nancy, France, Sept. 2-6, you can see him receive the George Box Award and hear him speak more about his upcoming book. Ron talks about this “golden age of analytics” as having attracted many disciplines. Quality information is necessary on so many fronts where opportunities for collaboration abound and where statisticians can uniquely contribute to obtaining better quality information, insights, and decisions. We hope you will hear more words of wisdom from Ron in this month’s episode of Analytically Speaking. ... View more

Stan Higgins says, “Without analytics you are relying on faith, hope and to some degree charity – will the customer wait for the next set of results?” Since my father was a chemistry professor, I have an appreciation of the chemist’s point of view. Chemicals are ubiquitous — in us, on us and around us. As the American Chemical Society states, “Chemistry is not limited to beakers and laboratories. It is all around us, and the better we know chemistry, the better we know our world.” As with many other disciplines though, chemistry must evolve to take advantage of more data and the methods to distill that data into knowledge. The Royal Chemical Society makes some predictions along these lines, a few of which are included here: “Chemistry will need to become more global, more interdisciplinary, and more collaborative." "There will be an enormous growth in scientific data, as well as the tools to manage, interrogate and analyse it.” This month’s featured guest in our Analytically Speaking webcast series, chemical industry innovator Dr. Stan Higgins OBE, is attuned to those predictions and sees the opportunities for analytics to create more value. After a 48-year career, Stan was awarded an Officer of the Order of the British Empire for his work promoting the United Kingdom’s chemical process manufacturing industry. He is the retired CEO of NEPIC (the North East Process Industries Cluster) where he contributed to the region’s noteworthy growth and global influence on the chemical, biotech and renewables industries. Stan is a Fellow of the World Society of Chemistry and received the Lifetime Achievement Award from the Chemical Industries Association. Though Stan’s subject-matter expertise is in specialty chemical manufacturing, his insights are relevant to other process-enabling industries like pharmaceuticals, biotech, semiconductors and advanced materials.  Stan cites a constant pressure to innovate in the chemical and chemistry-using industries. The pressure to speed up research, develop products more quickly to be more responsive to customers’ demands for improvements, to offer products which are more sustainable and less toxic are just some of the pressures stoking innovation. Stan sees key opportunities for analytics to alleviate some of that pressure — in research and development, production, maintenance, and beyond. Here are some highlights:  For R&D, experimentation has to be smart. With designed experiments, you can learn faster. Faster learning drives better decisions, reveals the sweet spots of opportunity, and ultimately, brings better products to market more quickly. Moving to production, Stan says while there is always the desire to make things as efficiently as possible, care has to be taken to meet health, safety, and environmental constraints. Since you want to address quality out of the gate as well, analytics plays an important role in process understanding and improvement. Stan explains that there are many variables—the quality and variability of raw materials, the durability and precision of the equipment, etc. Getting a handle on that variability, understanding what affects quality and how to improve it are more opportunities for analytics. Beyond production, Stan talks about the opportunities to capture more value in maintenance, a specialized area with their own data and processes. These range from preventive maintenance to improving equipment performance and beyond. Stan indicates that monitoring maintenance (and other aspects of production) has been facilitated with automated data collection, resulting in a great deal of data, much of which is often left untapped. As Stan says, “we produce data and never use it…, but people are beginning to realize they’ve paid for all this data.” To learn more about opportunities to create more value more quickly in chemistry and chemical-using industries, watch the interview with Stan. ... View more

Professor Peter Lenk of the University of Michigan is an expert on Bayesian methods. Peter Lenk has a richly storied journey — from influences in his childhood that led him to pursue a PhD in statistics to teaching and groundbreaking research for useful applications in business and economics. Peter is Professor of Technology and Operations and Marketing at the Stephen M. Ross School of Business at the University of Michigan. He has made many contributions to Bayesian inferential, forecasting, and semiparametric modeling methods. His teaching spans management decision analysis, predictive modeling and forecasting. He is also a fellow of the American Statistical Association, and has many other honors and awards. Peter is our guest on this month’s episode of Analytically Speaking. He shares what attracted him to Bayesian statistics — in part because it is prescriptive about what should be done in any situation.   We use JMP Text Explorer to visualize some of his publications, and it's no surprise that Bayesian terms are prominent. Though he has many publications and citations, the outlier cited the most is Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs, co-authored with DeSarbo et al. He also shares the serendipitous circumstances that led him to apply Bayesian methods to marketing applications. His first encounter with JMP (one of the very early releases) didn’t win him over. But a decade or so later, the University of Michigan adopted JMP, and Peter has given us a wonderful customer story on what he likes about teaching with JMP. (He tells his students it’s a treasure hunt to see what’s behind the red triangles.) You can learn more about what Peter has to share by watching this month’s episode of Analytically Speaking, and check out the customer story, Sophisticated analytics made easy.   ... View more

We recently had the pleasure of hosting Greg Nelson, CEO and founder of the healthcare analytics company, ThotWave (also a JMP partner) on Analytically Speaking. Greg also teaches analytics at the nursing and business schools at Duke University and has recently authored the book, The Analytics Lifecycle Toolkit: A Practical Guide for an Effective Analytics Capability.          Greg was kind enough to share an early copy of his book, which I found uniquely informative. With so many books for the analytically minded, not many factor people, process, and organizational culture in to the extent this one does. The book is well-organized and reflects Greg’s years of experience, wisdom, and best practices from his familiarity with the data-to-information continuum.  John Sall, the chief architect of JMP and Executive VP of SAS, has a quote on the book cover: “This book condenses a lot of deep thinking on the wide field of analytics strategy. Analytics is not easy – there are no quickie AI/BI/ML shortcuts to understanding your data, your business, your processes. You have to build a diverse team of talent. You have to respect the hazards of 'fishing expeditions' that may need false-discovery-rate adjustments. You should consider designed experiments to get the true behavior of a process, something that observational data may hint at, but not provide complete understanding. There are dimensions of data wrangling, feature engineering, and data sense-making that all call for different skills. But with deep investment in analytics comes deep insight into processes and tremendous opportunity for improvements. This book puts analytics in the context of a strategic business system, with all its dimensions.” Greg shares many valuable ways to make the most of your analytic assets; among these, we discussed: Data for purpose versus data for potential Analytics Lifecycle in support of a decision life cycle Exceptional analytic talent and how to retain it The importance of design thinking Analytics as a strategy to support change At the end of the day, Greg says, analytics is not just about value creation in terms of time and money. It’s also about learning, gaining insight/understanding, and being more informed to make better decisions — even if the decision is what experiment to implement next. You can hear the full interview at your convenience. ... View more

How do you decide if an activity is too risky? How do you know how risky some of your actions are? In the 12 months following the 9/11 terrorist attacks in the US, many Americans avoided flying, choosing to drive instead. But as a result of this increased time on the roads, and especially for long-distrance travel, about 1,600 more people died in crashes on US roads. This is but one of the many compelling stories that Gerd Gigerenzer, Director of the Harding Center for Risk Literacy at the Max Planck Institute for Human Development, shares about the importance of understanding risk to inform better decisions.  In his Discovery Summit Europe plenary speech, “Risk Literacy: The Cement of Society,” h e shows why we need to be more risk literate and understand the distinction between risk and uncertainty in finance, health, and digital media. Here are some of his key recommendations.   Focus on the known hazards and work to change behaviors, such as distracted driving and overuse of digital media, Gerd Gigerenzer says. For decisions regarding finance Use simple heuristics and opt for simpler, more transparent, and safer algorithms. Know the state space. Gerd uses the "turkey illusion" to illustrate that we need to allow for possibilities we haven't witnessed. Each day, a turkey may wonder if it will be killed or fed — on the hundredth day, the turkey may have a high expectation of being fed, but on the day before Thanksgiving, that is not the case. Similarly, financial models need to allow for unknown outcomes. For decisions regarding health Know the relative risk instead of absolute risk. He criticizes the media for profiting from headlines that are misleading, such as those that overstated the risk of thrombosis from the use of birth control pills; inaccurate media coverage led many women to stop taking their birth control pills and resulted in unwanted pregnancies.  Ask for the absolute numbers. Gerd gives an example of the increased risk of colon cancer from eating processed meat. Vegetarians have a 5% lifelong risk of getting colon cancer. If they eat 50 grams of processed meat, the risk increases to 5.9%. Understand the harms of treatment in studies, which can be substantial. For decisions regarding digital media  Focus on the known hazards and work to change behaviors, such as distracted driving and overuse of digital media. Distracted drivers cause accidents, with 1,000 people injured and 10 killed every day in US. Technology is having adverse effects on emotional life and cognitive development, as loneliness, depressive thoughts and anxiety have increased compared with previous generations.  Governments can be too paternalistic, wrongly conveying the level of risk involved in taking an action or not taking an action, he says. When we are true to the data and we convey the risks in an easy-to-understand way, everyone will have the information needed to make better decisions affecting their money, health, safety and well-being.  His book, Risk Savvy:  How to Make Good Decisions, provides more context and background on why we should all be more risk savvy. Certainly, education is key, but we also need to continually question what the risks really are. Change is the one constant, so we must reassess our risk literacy on an ongoing basis.  We need to invest the time in teaching risk literacy and statistical thinking. ... View more

Data preparation gets even easier in the new version of JMP. A 2014 New York Times article, For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights, seems to have generated a lot of interest in the title “data janitor.” There is even a Wikipedia page for it. Whatever you call it: Data janitoring, data wrangling or simply data cleanup, it's sadly pretty descriptive of the drudgery often associated with data preparation for analysis. In the same way the amazing late Hans Rosling described how the "magic" washing machine freed more time for his mother to pursue other things in his The Magic Washing Machine TED Talk, aren't there some innovative ways to automate "data washing"? Shouldn’t we expect a little less data drudgery and a little more time to pursue other things in this era of automation? We think so. JMP has a long history of easing the burden of data cleanup work. JMP 14 contributes more data wrangling features and improvements to speed your data discoveries and get to the "good stuff" of data analysis faster — visualization, modeling and building dashboards and reports to sharing your findings. After getting JMP 14 next month, be sure to explore some of these must-see data cleanup time savers. Multiple-file import The new multiple-file import capability allows you to load dozens or hundreds of files and concatenate them into a single JMP data table  — all without scripting. You can filter files by size, date, name and type. A common use-case for this is concerting a folder files that you might want to perform text exploration on  — a folder full of repair transcripts for example, with one repair per file. The import that I want to accomplish is map each document to a cell in a JMP data table as a pre-processing step before text analysis. With mutliple file import, you can choose to import to single column making this once time-consuming data pre-processing step easy. Jason Brinkley, Senior Associate at Abt Associates, participated in our JMP 14 early adopter program and was happy about the time saved with this new feature:   “I recently had a big project where someone had downloaded over 150 data files scrapped together from the web in batches.  These files were big and unstructured, but worst of all were downloaded at two levels (individual and group levels). There was no way to know which files had data at each level from the file names.  The 'Multiple File Import' found in JMP 14 was a huge time saver in that it imported all the data, skipping the nuisance info at the top of the data files (website info and web page headers) and automatically stacked the files into the individual and group levels.  The processing took about 30 minutes but in the end, I had two files that were easy to clean and make ready for analysis.  I feel like this particular effort would have required significant coding in other platforms.” Recode Recode in the Cols menu has been around for a long time, but it continues to be improved in every new version of JMP. JMP 14 brings even more power to wrangle categorical and numeric data with enhancements like improved Text Parsing, Recode results column import, Recode numeric continuous columns, support for multiple-response columns, replace string (with or without custom regular expression parsing) and more. I often look at titles of people who register for JMP events to see what kinds of scientists, engineers, researchers, and other analysts these events attract. The support for multiple response columns in Recode is especially nice when looking at titles with credentials. If you had a column like the one below and wanted to separate the credentials, it involved many steps — text to columns, stacking, recoding, splitting, combining, and then pasting. Now, you can just designate the column as a multiple response modeling type, recode, and you are able to perform the data cleanup tasks with ease.   Before designating the column’s Modeling Type as Multiple Response.   Change from Nominal to Multiple Response for Modeling Type.       Fewer categories after recoding the credentials as multiple response.   Still want more? More data prep help There are many other additions in JMP 14 to lessen the number of clicks and streamline the discovery process, such as: Virtual join improvements that offer access to data in one table by another table with just a few clicks.  New column utilities that make it easier to create and work with Value Labels. Ease of finding duplicate rows. Other workflow improvements like preference searching and filtering and the ability to preview results in the Formula Editor. We hope these highlights pique your curiosity about how JMP 14 can make your data janitoring more enjoyable —  and give you more time to pursue other things.  ... View more

JMP has a long track-record of adding useful methods to aid the work of scientists, engineers, researchers and analysts. What inspires the addition of these new methods? Often, there is a story behind the inspiration to include new methods, and in the case of A-optimal designs, it starts with the dissertation of a PhD student named Jon Stallings, who is now Assistant Professor of Statistics at North Carolina State University. Our own Bradley Jones, Distinguished Research Fellow in the JMP division, served on the dissertation committee for Jon, whose dissertation, General Weighted Optimality of Designed Experiments, includes the motivating use case for adding A-optimality criteria to the custom design capabilities in JMP. Jon shared his thoughts on the inclusion of A-optimality designs in JMP 14:   “Calling a design optimal can be very misleading for practitioners. It is important for practitioners to understand that a design is found to be optimal with respect to a summary measure of that design’s analysis performance. The summary comes with a loss of information about that design’s performance and practitioners should be aware of how the optimality criterion relates to their analysis goals. The D-criterion is by far the most popular criterion and works very well for response surface designs which place equal emphasis on estimating main effects and interactions. If one wanted to place greater emphasis on estimating some effects over others, the D-criterion would not be appropriate. Including weighted-A criteria in JMP 14 gives practitioners more flexibility in design selection and allows them to tailor a design to their specific goals. JMP’s powerful Custom Design platform allows practitioners to quickly generate and compare competing designs under these criteria and become more involved in design selection.” As Brad explains, “A-optimal and D-optimal designs are similar, but A-optimal designs allow for putting different emphasis on groups of parameters through weighting and the optimality criteria is easy to understand. Depending on the situation, you may want to emphasize main effects more, or second-order effects and weighted A-optimal designs give you the flexibility to do that.” In this example comparing A-optimal and D-optimal designs, the A-optimal design has more desirable properties. The D-optimal design has messier correlation structures and the design diagnostics for the A-optimal design are much better.     This is a simple example, but it conveys the potential for A-optimal designs to give you flexibility to emphasize some effects over others and create a design that can better meet your needs. A-optimality criteria is a relatively new research area in design of experiments, and we are pleased to include it in JMP 14. You can expect more detailed blog posts on new DOE features later in the year. ... View more

The decision-making culture at Procter & Gamble is characterized by a love of data, says Cy Wegman. Cy Wegman is an inspiration. He had a 38-year career of creating value and greater analytic bandwidth at Procter & Gamble, where he was the Empirical Modeling and Optimization Global Leader. Now that he’s quasi-retired, he helps other organizations create value and analytic bandwidth since founding SY64, an analytics consultancy specializing in modeling, optimization, design of experiments, and data analysis. Because his efforts at P&G saved hundreds of millions of dollars, Cy won the highest award a P&G engineer can receive: the PRISM award. He is a member of the P&G Engineering PRISM Society (Professional Recognition of Individual Sustained Mastery). Only 2 percent of the company’s engineers receive this award. His analytic contributions to numerous projects across several product lines and his global training and mentoring of thousands of colleagues leave a lasting legacy at P&G. His colleagues view his departure as the end of an era. I had the pleasure of interviewing Cy for this month’s episode of Analytically Speaking. In our chat, Cy shares how the culture at P&G supports better decision-making — how much the love of data and the love of learning factor into it, how team decisions are encouraged, the importance of mentorship and foundational leadership values like integrity, trust, patience, kindness, and more.  His enthusiasm for technical problem-solving and overcoming obstacles shines through when he discusses highlights from his long, rewarding career at P&G. He tells about working on an optimization project where he was able to increase production, improve quality, AND decrease costs. He has great appreciation for the tremendous changes in designed experiments throughout his career — and he even said this made his career! He also makes a case for why every analyst and data scientist should watch the movie Hidden Figures. I hope you will tune in to hear more of the wise words Cy has to share in this episode of Analytically Speaking.  ... View more

We should work on educating people to become risk literate, says Professor Gerd Gigerenzer. Can the general public learn to deal with risk and uncertainty? Or should authorities steer people’s choices in the right direction? Gerd Gigerenzer, Director of the Harding Center for Risk Literacy at the Max Planck Institute for Human Development in Berlin, and author of Calculated Risks, Gut Feelings: The Intelligence of the Unconscious, and Risk Savvy: How to make good decisions, explores these questions in his keynote talk at Discovery Summit Europe, 14-15 March in Frankfurt. Gigerenzer calls risk literacy “the cement of society” and argues that even the experts themselves often lack it. “Many judges, doctors, journalists and politicians alike do not understand statistical evidence and can be fooled into wrong conclusions without noticing," he says. I asked him a couple of questions to preview his talk, Risk Literacy: The Cement of Society. You say that the general public lacks risk literacy, and sometimes experts do, too. What are some steps we can take personally to improve our risk literacy? Start with Benjamin Franklin’s piece of wisdom “In this world there is nothing certain but death and taxes.” This holds in the digital age as well – it would be an illusion to believe that Big Data, innovative financial products, or artificial intelligence provide certainty. Next, make an effort to improve your statistical thinking – a good way is to learn the major tricks that medical and financial institutions use to mislead customers (unstatistik.de). Finally, consider Immanuel Kant’s motto for the Enlightenment, “Sapere aude!” That is, have the courage to know.  You mention that sometimes people need “nudges” to steer our behavior toward what is best for us. Can you give us an example of how this works? The philosophy of nudging assumes that people lack rationality and that there is little hope of remedying this. In its view, the government, therefore, needs to step in and steer people like sheep. That is not my vision of the 21st century! Instead of governmental paternalism, we should work on educating people to become risk literate.  Here is an example. Consider the appointment letters sent in many countries to women above age 50 for mammography screening. These letters contain a fixed time and location. This default booking is a nudge that exploits inertia — those receiving the letter might not make the effort to actively sign up or, similarly, to decline the appointment. Furthermore, in the letters and pamphlets encouraging screening, it is often stated that early detection reduces breast cancer mortality by 20 percent. That figure is a second nudge that takes advantage of those with limited understanding of statistics. As it stands, screening reduces the number of women who die from breast cancer from about 5 to 4 in 1,000 (after 10 years), which in absolute numbers amounts to a risk reduction of 1 in every 1,000. But to look more impressive, this figure is typically presented as a relative risk reduction of 20 percent, often rounded up to 30 percent. Many people automatically assume this to mean that 20 to 30 lives are saved out of 100, which translates into 200 to 300 per 1,000. Here, nudging exploits people’s inertia and lack of risk literacy to steer them into screening, which is a lucrative business. The alternative that I prefer is to provide people with honest and transparent information about benefits and harms of screening or other medical therapies, so that everyone can make informed decisions themselves. This is the difference between nudging and education. We need competent citizens, not more paternalism.   Want to join us in Frankfurt to hear this speech by Professor Gerd Gigerenzer? It's not too late to sign up. ... View more

"What would be a life that is worth living?" was a question that researcher Vic Strecher and his family confronted. As a behavioral scientist, Victor J. Strecher, Professor and Director for Innovation and Social Entrepreneurship at University of Michigan School of Public Health and the founder of JOOL Health, has thought a lot about purpose and well-being. His path to thinking about and articulating his own purpose is a poignant one.  His book, Life on Purpose, is both his journey as well as the data and science behind the importance of having a sense of purpose in life. The evidence is compelling. For example, people who have a stronger sense of purpose: Live longer and have higher levels of well-being. Are more emotionally resilient and experience post-traumatic growth instead of post-traumatic stress following horrific events. Have fewer heart attacks and strokes. Are less likely to develop Alzheimer’s disease as they transition to retirement. Vic shared many results of replicated research on why we should all have a strong sense of purpose in life. Part of Vic’s own purpose is empowering others to think about and articulate their own purpose. He is sincere when he says “JMP changed my life.” As a behavioral scientist, Vic appreciated how JMP empowered him to get more done with his data.  He then went on to found JOOL Health to help others be their best selves. To have a life worth living, you need energy and willpower, which are fueled by S-P-A-C-E:  Sleep (increasingly important as research shows) Presence Activity Creativity Eating One other popular part of Vic's plenary was playing a short recording of "This Is The Application – A new manifesto of well-being" written and performed by poet Sekou Andrews.  The recording is also available on JOOLHealth.com. For the first time in the history of JMP Discovery Summit, attendees gave a standing ovation. All were moved by Vic’s journey. I highly recommend watching the recording of his speech.  ... View more

Lucky and unlucky people see the same event differently, says Richard Wiseman. We have John Sall, the chief architect of JMP, to thank for making us aware of Richard Wiseman and his research on luck in his book The Luck Factor. We were so impressed and entertained with his plenary talk at JMP Discovery Summit Europe last year that we invited him to speak at JMP Discovery Summit Japan last year and next month at JMP Discovery Summit in St. Louis.  Richard has a remarkable title: Professor of the Public Understanding of Psychology in the United Kingdom. And he is also a professional magician, and the author of several journal articles and best-selling popular psychology books. His YouTube channel, Quirkology, is one of the most watched in the UK. I asked Richard a few questions about his research on luck.  How did your research on luck come about, which led to your book The Luck Factor? I became interested in the psychology of superstition, and why we all cross our fingers, avoid walking under ladders, and so on. When I interviewed people, they started to talk about being lucky and unlucky. At the time, psychologists had pretty much ignored the idea because they had thought that luck was either just chance or people were fooling themselves. I managed to gather together 1,000 lucky and unlucky people, and immediately we started to see differences in the way they were thinking and behaving. What is it about lucky people that helps them notice things unlucky people miss? Much of it is to do with mood. When you are in a good mood your perception becomes very expansive, and you start to notice things that you might otherwise miss. Maybe you bump into someone at a party and realise that they would be perfect for a project that you are working on, or you see an advertisement in a newspaper that you would have otherwise skipped over. In contrast, unlucky people tend to be very anxious, and so tend to just focus on what they are looking for, rather than what is right in front of them. Please share some of your favorite findings from researching luck? I guess my favourite finding is the way in which lucky and unlucky people view the same event. Time and again, people would experience the same life event but see it in totally different ways. So, for instance, you might have both a lucky and an unlucky person win a small prize in a lottery. The lucky person would be delighted because they won something, and the unlucky person would be upset that they didn’t win a bigger prize. I then started to investigate the mechanisms behind the effect and the psychology is fascinating. Lucky people are grateful for what they have, whereas unlucky people are very good at imagining a world in which they have far more and are therefore disappointed with how things have worked out. ----------------- Richard Wiseman is a keynote speaker at Discovery Summit 2017. You can learn more about him and read the abstract of his talk "The Luck Factor." You might also like to watch his interview on Analytically Speaking. ... View more

Vic Strecher wants to democratize the application of the research on purpose to help make people's lives better. He is Professor and Director for Innovation and Social Entrepreneurship at University of Michigan School of Public Health, but he is also the founder of JOOL Health. Why should we have a purpose in life? How is purpose related to well-being? Can thinking about our purpose help us make better decisions? These are the kinds of questions Victor J. Strecher ponders and researches. I can’t remember exactly how his book, Life on Purpose, came on my radar last year, but I am so glad it did! When I was part-way through the book, the way he wrote about the data and all the research he and others have done in the area of well-being made me wonder if Vic might be a JMP user. As it turns out, Vic is a longtime JMP user! Having also seen his TEDx Talk and his TEDMED Talk, I knew he would be an excellent plenary speaker for JMP Discovery Summit. We’re so glad we got to him before his calendar filled. Not only is he Professor and Director for Innovation and Social Entrepreneurship at University of Michigan School of Public Health, but he is also the founder of JOOL Health, which has him traveling quite a bit. Vic was kind enough to answer a few questions on purpose. Please share some of your research on the power of a strong sense of purpose in life. Our research group has used functional magnetic resonance imaging (fMRI) to examine neural activation from purposeful thinking in sedentary adults, then subsequent changes in physical activity from smartphone-based messages. We see a clear and consistent activation of the ventro-medial prefrontal cortex (VMPFC) — a part of the brain associated with self-related processing. The more active this part of the brain gets, the more behavior change we see. We’ve also found in epidemiologic cohort research that purposeful people are more likely to use preventive health services, have fewer hospital visits, and have more health behavior changes. This is after statistically adjusting for demographic, health status, and health behavior factors. Having a strong purpose in life or, in experimental studies, just thinking about or writing about your purpose, has produced a wide range of positive effects. The results are both strong and consistent. Of the authors you’ve read and mentioned in your book, Life on Purpose, which most shaped your thinking on having a purpose? It would have to be Viktor Frankl, a physician who was a prisoner in Nazi concentration camps during World War II. His observations of people who maintained a purpose in life are vivid illustrations of the power of purpose. His efforts to help people live more purposeful, self-transcending lives, is an inspiration for me and for millions of others. What gave you the idea for your latest venture, JOOL Health? I wanted to democratize what Viktor Frankl was doing to help people find and align with their purpose on an individual level. Extending his efforts to the digital world, where we can now help a person compose their life purpose, change it at any time, and learn what gives them greater alignment with their purpose — all by moving electrons around — is a great opportunity. ----------------- You can learn more about Vic and read the abstract of his talk "Life On Purpose: Integrating Predictive Modeling, Big Data, and Mobile Technology for Health and Well-Being." You can also watch an Analytically Speaking interview on Nov. 15 or watch it on demand after that.   ... View more

Mike Adams, Professor Emeritus, Applied Statistics at The University of Alabama If you are a regular reader of the JMP Blog, you may have seen a post before with a quote from September’s featured guest on Analytically Speaking. Though recently retired as Professor of Applied Statistics for more than 25 years at the University of Alabama in the Culverhouse College of Commerce, Mike Adams has begun an active retirement because when you have a passion for applied statistics as Mike does, it is never something you retire from completely. He is currently serving on the JMP Discovery Summit Steering Committee, where he and others have done a great job selecting speakers for the upcoming Discovery Summit in St. Louis in October. And on Sept. 20, we are pleased to host him as our featured guest on Analytically Speaking. In addition to years of teaching applied statistics like design of experiments, statistical quality control and data mining, Mike has considerable experience showing the usefulness of applied statistics with organizations such as NASA, Mercedes-Benz USA, U.S. Veterans Administration, Wal-Mart, AT&T, and others. His passion for applied statistics extends to other educational areas like the Alabama Quantitative Literacy Program. At JMP Discovery Summit in Amsterdam last year, Mike’s paper, Have You Heard the Expression…?, was motivated by something that could be applied and very useful.   It begins with a fun quip: “Data sets are a little like people. Some are short and wide, others long and narrow. Most of them have ‘issues’.” But, it turns serious when he shares his motivation to learn JMP Scripting language (JSL) and use JMP’s Expression modeling type. A friend’s life was significantly disrupted by the devastating tornado of April 27, 2011, that left a wide swath of destruction in Tuscaloosa. His applied perspective comes through when he made the prototype surface a trove of useful information for would-be responders in disaster recovery. Sadly, such an application as Mike prototyped for the paper could be put to use in the wake of Hurricane Harvey. Mike’s students were fortunate to have had someone so passionate about applied statistics (and witty) who sought to give them skills that would ultimately help them find jobs more easily. In 2013, he oversaw two student teams who received honors at the SAS Analytics Shootout national data mining competition — one took first place and the other an honorable mention.  Please join us to hear more about Mike’s successful efforts to make analysis relevant and useful to students and organizations in a changing data landscape. We'll talk about some of the best and worst things in statistical education, the evolution of statistics over the past several decades and more.  If you can’t join Sept. 20, you can always catch the archived interview along with other Analytically Speaking episodes you may find interesting. ... View more

Elea McDonnell Feit is the guest on Analytically Speaking for August and will discuss experimentation. Our featured guest for the Aug. 16 episode of Analytically Speaking is one you won’t want to miss. Elea McDonnell Feit is currently Assistant Professor of Marketing at Drexel University. Her degrees in math, engineering, and marketing contribute to her unique perspective, enabling her to take on a wide variety of challenges.  She has developed new quantitative methods and put them into practice in different industries and applications. She’s built an academic-industry partnership program and collaborated with organizations to solve challenging problems (with challenging data). And her research is featured in top-tier journals.  In addition, Elea advocates ongoing learning for practitioners to keep apprised of new methods and best practices, regularly teaching workshops and tutorials. She is active in practitioner conferences and has co-authored the very popular and highly rated book, R for Marketing Research and Analytics. She has another book in the works on business experiments that we are looking forward to seeing. "You need to learn two things: writing and statistics." That's the guidance her mother gave her and that she took to heart. And it's good advice for all. Given her areas of interest, you can expect to hear more about the growing importance of experimentation in business, the relevance of marketing analytics to many (including engineers), choice modeling, the need for more data detectives, and more. Please join us to hear more from this accomplished expert. If Aug. 16 isn’t convenient to watch the conversation with Elea, you can always catch the archived version along with other episodes of Analytically Speaking whenever you like. ... View more