inst/help/Evaluation.md

In koenderks/JASP-for-Audit: Audit Module for JASP

-Independent Samples T-Test

The independent samples t-test allows you to test the null hypothesis that the population means of two independent groups are equal

Assumptions

• Continuous dependent variable
• The observations in both groups are a random sample from the population
• The dependent variable is normally distributed in both populations
• The population variances in the two groups are homogeneous

Default Options

Hypothesis:

• Group 1 ≠ Group 2: Two-sided alternative hypothesis that the population means are equal
• Group 1 > Group 2: One-sided alternative hypothesis that the population mean of Group 1 is larger than the population mean of Group 2
• Group 1 < Group 2: One-sided alternative hypothesis that the population mean of Group 1 is smaller than the population mean of Group 2

Equality of Variances:

• Assume equal: Assume that the variances of the two groups are equal. The pooled variance is used as an estimate of the population variance.
• No assumption: Do not assume that the variances of the two groups are equal. The degrees of freedom will be computed using the Welch approximation.
• Report both: Report results assuming and not assuming the equality of variances.

Missing Values:

• Exclude cases analysis by analysis: In case of multiple t-tests within a single analysis, each test will be conducted using all cases with valid data for the dependent variable for the particular t-test. Sample sizes may therefore vary across the tests.
• Exclude cases listwise: In case of multiple t-tests within a single analysis, each t-test will be conducted using only cases with valid data for all dependent variables. Sample size is therefore constant across the tests.

Default Output

Independent Samples T-Test:

• t: t-value
• df: Degrees of freedom
• p: p-value

Additional Options

Test inequality of variances:

• Levene's test for homogeneity of variances

Additional Statistics:

• Mean difference: Difference in sample means and the standard error of the difference in means
• Effect Size: Cohen's d measure of effect size
• Confidence interval: Confidence interval for the difference in population means
• Interval: Coverage of the confidence interval in percentages
• Descriptives: Sample size, sample mean, sample standard deviation, standard error of the mean for each group
• Normality tests: Shapiro-Wilk test of normality
• Descriptive plots: Displays the sample means and the confidence intervals for each group
• Confidence interval: Coverage of the confidence intervals in percentages
• Vovk-Sellke Maximum p-Ratio: The bound 1/(-e p log(p)) is derived from the shape of the p-value distribution. Under the null hypothesis (H0) it is uniform(0,1), and under the alternative (H1) it is decreasing in p, e.g., a beta(α, 1) distribution, where 0 < α < 1. The Vovk-Sellke MPR is obtained by choosing the shape α of the distribution under H1 such that the obtained p-value is maximally diagnostic. The value is then the ratio of the densities at point p under H0 and H1. For example, if the two-sided p-value equals .05, the Vovk-Sellke MPR equals 2.46, indicating that this p-value is at most 2.46 times more likely to occur under H1 than under H0.

Additional Output

Independent Samples T-test:

• Mean difference: Difference in sample means
• SE Difference: Standard error of the difference in means
• Cohen's d: Cohen's d measure of effect size
• x% Confidence Interval: Lower and Upper bound of the x% confidence interval for the difference in population means

Group Descriptives:

• N: Sample size
• Mean: Sample mean
• SD: Sample standard deviation
• SE: Standard error of the mean

Test of Normality (Shapiro-Wilk)

• W: W test statistic
• p: p-value

Descriptive Plots:

• Displays the sample means (black bullet), the x% confidence intervals (whiskers) for each group

References

• Moore, D. S., McCabe, G. P., & Craig, B. A. (2012). Introduction to the practice of statistics (7th ed.). New York, NY: W. H. Freeman and Company.
• Sellke, T., Bayarri, M. J., & Berger, J. O. (2001). Calibration of p values for testing precise null hypotheses. The American Statistician, 55(1), 62-71.
• Whitlock, M. C., & Schluter, D. (2015). The analysis of biological data (2nd ed.). Greenwood Village, Colorado: Roberts and Company Publishers.

koenderks/JASP-for-Audit documentation built on June 22, 2019, 2:57 a.m.