This document provides a flowchart to guide users in selecting the appropriate hypothesis test based on the type of data (continuous or discrete variables), number of variables, assumptions of the data, and other factors. It outlines hypothesis test categories and provides decision trees to navigate through options for continuous and discrete dependent and independent variables, from parametric to non-parametric tests. It also includes sections on control charts and considerations for chart selection based on data type, subgroup size, and sampling versus census data.

1. Hypothesis Test Selection Guide Place file in Powerpoint’s “Slide Show” mode to navigate with interactive links Click here to begin Ver 1.2

2. Hypothesis Test Categories Continuous Y, Continuous X(s) Tests Continuous Y, Discrete X(s) Tests (Continuous 1 Var) Discrete Y, Continuous X(s) Tests Discrete Y, Discrete X(s) Tests (Discrete 1 Var) Continuous Y? Y N Continuous X(s)? Continuous X(s)? Y / Both N / Both N Y Start

3. Discrete Y, Continuous X(s) Binary, Ordinal, or Nominal Y? Binary Logistic Regression Binary Nominal See MBB Nominal Logistic Regression Ordinal Logistic Regression Ordinal Return to Start

4. Discrete Y, Discrete X(s) No. of levels?  2 Goodness of Fit (   2 Test of Association (  Test of Two Proportions (  Done Test of One Proportion (  Testing vs. a Target Value(s)? Y N Y = 2 X = 2 Binomial, Ordinal, or Nominal Data Kolmorgorov Smirnov (  Done Ordinal Nominal Binomial Y or X > 3 Minitab generate warning? Fishers Exact Test (  Y N Return to Start

5. Continuous Y, Discrete X(s) Test for Normality ( Shape = normal) Residuals Normal? ResidualsEqual Variance? Residuals Stable? Y Y See MBB N N N Y Done Testing vs. a Target Value(s)? Y N 1 Sample t  1 Sample Sign (m = #) Data Symmetric? 1 Sample Wilcoxon (m = #) Y N Not Normal Normal Done No of X’s? 1 > 2 2 Sample t (Assume equal variance) (  No of levels? 2 Data Paired? N Y > 3 Paired t (  See MBB 1 Way ANOVA (  General Linear Model (1) (  Go to “B” Perform Box-Cox Transform and Reanalyze Data already Transformed? N Y Return to Start (1) See MBB if data has both continuous and discrete X’s

6. Continuous Y, Discrete X(s) cont. Done No of X’s? 1 2 Large number of Outliers? N Y Moods Median (m = m) B Kruskal Walace (m = m) Friedmans Test (m = m) No of Levels? 2 Mann Whitney (m = m) > 3 Return to Start Use raw (untransformed) data for these tests.

7. Continuous Y, Continuous X(s) No. of Xs Simple Linear Regression (b n = 0) Multiple Linear Regression (b n = 0) 1 >1 Residuals Normal? ResidualsEqual Variance? Residuals Stable? Y Y See MBB N N N Y Done Return to Start Convert discrete X’s to dummy variables if needed.

8. Control Charts Continuous or Discrete Data ? Discrete Continuous Charting Proportion Defective or No. Defects per unit measured? No. Defects/unit Proportion Defective Individuals Moving Range Sample of Data Constant Subgroup Size ? YES NO c Chart u Chart Constant Subgroup Size? np Chart NO YES p Chart Individuals Moving Range Use X-Bar S when subgroup size is > 8 Data uses Subgroups or Individuals? Subgroups (n>1) Individuals (n=1) X-Bar R X-Bar S Cyclic or non-cyclic pattern? Non-cyclic EWMA or CUSUM Cyclic Charting Sample or Census of Data? Charting Sample or Census of Data? Census (All Data) Sample of Data Last Slide Viewed Return to Start