The choices on this tab vary a bit depending on which test you chose on the first tab.

Calculations

The default choices for the calculation options will be fine for most people (two-tailed P values, 95% confidence intervals, and difference computed as the first column minus the second).

•One- or two-tailed P value. Choose a two-tailed P value, unless you have a strong reason not to.

•Report differences as. This determines the sign of the difference between means or medians that Prism reports. Do you want to subtract the second mean from the first, or the first from the second?

•Confidence level. 95% is standard, but you can pick another degree of confidence.

Graphing options

The options available in this section depend on which test you chose on the first tab. They can be useful to view the data with more depth, but none are essential to beginners, with the possible exception of the “Estimation Plot”.

•Graph differences (paired). The paired t test and Wilcoxon matched pairs test first compute the difference between the two values on each row. This option creates a table and graph showing this list of differences

•Graph ranks (nonparametric). The Mann-Whitney test first ranks all the values from low to high, and then compares the mean rank of the two groups. This option creates a table and graph showing those ranks. The Wilcoxon first computes the difference between each pair, and then ranks the absolute value of those differences, assigning negative values when the difference is negative.

•Graph correlation (paired). Graph one variable vs. the other to visually assess how correlated they are.Graph residuals. This option is only offered for unpaired data. To create the new residuals table, Prism computes the difference between each value and the mean (or median) of that column.  Inspecting a graph of residuals can help you assess the assumption that all the data are sampled from populations with the same SD.

•Graph confidence interval of differences between means (Estimation Plot). This option generates a graph that includes a scatterplot (or violin) of the original data. Additionally, this graph includes a third dataset plotting the difference between means and 95% CI (for an unpaired test), or the mean of differences and a 95% CI (for a paired test). Estimation plots are very useful for visually assessing the results of t tests. Read more about how to understand and use Estimation plots.

Additional results

These four choices are not selected by default. The second choice (AIC) is for special purposes. The other three might be useful even to beginners.

•Descriptive statistics. Check this option, and Prism will create a new table of descriptive statistics for each data set.

•Also compare models using AICc.  Most people will not want to use this, as it is not standard. The unpaired t test essentially compares the fit of two models to the data  (one shared mean, vs. two separate group means). The t test calculations are equivalent to the extra sum-of-squares F test. When you check this option, Prism will report the usual t test results, but will also compare the fit of the two models by AICc, and report the percentage chance that each model is correct.

•Nonparametric tests. Compute the 95% CI for the difference between medians (Mann-Whitney) or the median of the paired differences (Wilcoxon). You can only interpret this confidence interval if you make an additional assumption not required to interpret the P value. For the Mann-Whitney test, you must assume that the two populations have the same shape (whatever it is). For the Wilcoxon test, you must assume that the distribution of differences is symmetrical. Statistical analyses are certainly more useful when reported with confidence intervals, so it is worth thinking about whether you are willing to accept those assumptions. Calculation details.

•Wilcoxon test. What happens when the two matching values in a row are identical? Prism 5 handled this as Wilcoxon said to when he created the test. Prism offers the option of using the Pratt method instead. If your data has lots of ties, it is worth reading about the two methods and deciding which to use.