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# Chapter 12. Testing More Than Two Samples

In This Chapter

• Why multiple t-tests won't work

• Introducing ANOVA

• What to do after an ANOVA

• Working with repeated measures

• Performing a trend analysis

Statistics would be limited if you could only make inferences about one or two samples. In this chapter, I discuss the procedures for testing hypotheses about three or more samples. I show what to do when samples are independent of one another, and what to do when they're not. In both cases, I discuss what to do after you test the hypotheses.

I also introduce Excel data analysis tools that do the work for you. Although these tools aren't at the level you'd find in a dedicated statistical package, you can combine them with Excel's standard features to produce some sophisticated analyses.

# Testing More Than Two

Imagine this situation. Your company asks you to evaluate three different methods for training its employees to do a particular job. You randomly assign 30 employees to one of the three methods. Your plan is to train them, test them, tabulate the results, and make some conclusions. Before you can finish the study, three people leave the company — one from the Method 1 group, and two from the Method 3 group.

Table 12-1 shows the data.

Table 12.1. Data from Three Training Methods

Method 1

Method 2

Method 3

95

83

68

91

89

75

89

85

79

90

89

74

99

81

75

88

89

81

96

90

73

98

82

77

95

84

80

Mean

93.44

85.20

75.25

Variance

16.28

14.18

15.64

Standard Deviation

4.03

3.77

3.96

Do the three methods provide different results, or are they ...

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