See how to:

• Understand the difference between a Null and an Alternate Hypothesis, the two conditions that we want to investigate
  • Null Hypothesis - generally the status quo, e.g. There is NO DIFFERENCE in the population means from which these two samples were drawn
  • Alternative Hypothesis - the complement of the Null, e.g. There IS A DIFFERENCE in the population means
  • We can also insert Greater Than (or Less Than) instead of No Difference
  • Accepting Alternate Hypothesis is rejecting (or disproving) Null Hypothesis
  • When running tests, we look for proof that the Alternative Hypothesis is within our confidence level, and we can Refute (or Disprove) the Null Hypothesis
  • Caution: If we DO NOT reject the Alt, it DOES NOT mean that the Null is true and perhaps we didn't collect enough data to accept it. 
• Confidence Level is the level of surety involved with the hypotheses
  • Confidence Level needed can depend
    • Often, acceptable Confidence Level for making decisions is 95%.
    • Perhaps if you are looking at life-or-death decisions, you need to be 99.9% confident in the results
  • 95% confidence means  if you run statistical test 100 times, your should be right about 95 times
  • Alpha value is  1 - Confidence/100
• Compare and interpret Confidence Interval on a Mean Value
• Compare Mean to a Target Value
  • Analyze>Distribution and then Test Mean from red triangle
  • Interpret Quanitles, Summary Statistics, Means Test
• Compare two Means
  • Analyze>Distribution and then t-test from red triangle
  • Interpret t-test
• Compare more than Two Means
  • Analyze>Distribution and then Means/ANOVA from red triangle
  • Run Oneway analysis (ANOVA)
  • Interpret Summary of Fit, R-square, Analysis of Variance and Means for one ANOVA
• Hypothesis Testing for Equivalence – Used because when sample means are not different, we still can’t conclude that the population means were the same
  • Alternate Hypothesis is that Population Means are the same, within some specified margin
  • Null Hypothesis is that the Population Means MAY NOT BE the same within the margin.
  • Difference in Population Mean is outside the equivalence interval
  • Employ 2-sided t-tests (TOST)

Note: Q&A is interspersed beginning at ~ Time 20:05.

If you have questions for Jerry, type @JerryFish in your comment.

Resources

• About AICc and BIC 
• Fitting Normal Distribution documentation
• Mastering JMP video and resources for Selecting Proper Sample Size for Your Designed Experiment
• Two Sample Test for Proportions documentation

• SKP Statistics Knowledge Portal 

• STIPS course Statistical Thinking for Industrial Problem Solving

   

• JMP Wish List for submitting suggestions for enhancing JMP

Compare Mean to Target ValueCompare Mean to Target Value