How does Prism handle missing values?

GraphPad Prism handles missing values easily. When entering data, simply leave a blank spot for any value that is missing. Prism treats excluded values identically to missing values.

Prism never ever treats an empty cell as if you had entered zero -- it always knows that is a missing value. It will analyze the data if it can, and leave analysis results blank when it cannot.

The details of how Prism handles missing values differs for various statistical tests. 

Unpaired t or or the Mann-Whitney nonparametric test

These tests work fine with unequal sample size. Missing values are not a problem.

Paired t or Wilcoxon matched pairs test

Prism only analyzes rows where there are data for both conditions. If one value is missing, that subject (row) is ignored.

One-way ANOVA (ordinary) or the nonparametric Kruskal-Wallis test)

If you choose repeated measures, there can be no missing values. For ordinary one-way ANOVA, unequal sample size is fine.

Two-way ANOVA

See this FAQ.

Linear and nonlinear regression

Fitting lines and curves works fine with missing values. You can choose whether Prism fits the individual replicates or fits the means. If you choose to fit the means, each mean gets the same weight regardless of how many values were used to compute it. If you fit the individual replicates, then X values with more Y replicates get more weight than X values with fewer replicates.

Survival curves

Comparison of survival curves does not require equal sample size. If data are completely missing for any subject, simply don't enter data for that subject. But before deciding to leave data out, read about censoring which happens when you know the subject survived up until a certain point, but don't know what happened after that (or you know, but can't use the data because the experimental protocol wasn't followed). Prism handles censored data fine. Don't omit those subjects, enter the duration that they survived on the experimental protocol and mark that duration as censored.