No. The groups must be defined by columns. Enter data for one group into column A, another group into column B, etc..

Yes. After you click Analyze, you'll see a list of all data sets on the right side of the dialog. Select the ones you wish to compare.

Yes. Follow this example to see how. It is impossible to run repeated measures ANOVA or a nonparametric test from data entered as mean, SD (or SEM) and N. You can only choose an ordinary one-way ANOVA.

No. You should analyze all the groups at once with one-way ANOVA, and then follow up with multiple comparison post tests. An exception is when some of the 'groups' are really controls to prove the assay worked, and are not really part of the experimental question you are asking.

You can enter data as mean, SD (or SEM) and N, and Prism can compute one-way ANOVA. It is not possible to compute repeated measures ANOVA, or nonparametric ANOVA without access to the raw data.

No. ANOVA compares the difference among group means with the scatter within the groups, taking into account sample size. If you only know the means, there is no possible way to do any statistical comparison.

This is not a good idea. Choosing when to use a nonparametric test is not straightforward, and you can't really automate the process.

Not with ANOVA. Enter your data into a contingency table and analyze with a chi-square test.

One-way ANOVA, also called one-factor ANOVA, determines how a response is affected by one factor. For example, you might measure a response to three different drugs. In this example, drug treatment is the factor. Since there are three drugs, the factor is said to have three levels.

If you measure response to three different drugs, and two time points, then you have two factors: drug and time. One-way ANOVA would not be helpful. Use two-way ANOVA instead.

If you measure response to three different drugs at two time points with subjects from two age ranges, then you have three factors: drug, time and age. Prism does not perform three-way ANOVA, but other programs do.

If there are only two levels of one factor --say male vs. female, or control vs. treated --, then you should use a t test. One-way ANOVA is used when there are three or more groups (although the underlying math is the same for a t test and one-way ANOVA with two groups).

The term repeated-measures strictly applies only when you give treatments repeatedly to each subject, and the term randomized block is used when you randomly assign treatments within each group (block) of matched subjects. The analyses are identical for repeated-measures and randomized block experiments, and Prism always uses the term repeated-measures.

This question only applies to repeated-measures ANOVA. These tips might help:

•Previous versions of Prism assumed sphericity. Check the option to assume sphericity to match results from older versions.

•If you ask Prism not to assume sphericity, the P values will be larger but probably more accurate. Confidence intervals of multiple comparisons tests will be computed differently. Some will be wider and some narrower than they would have been if you had assumed sphericity.

•We suggest that, if in doubt, you choose to not assume sphericity.

•It sounds sensible to measure deviations from sphericity (with epsilon), and then use that value to decide whether or not the ANOVA should assume sphericity. But statisticians have shown this approach works poorly. You need to decide based on experimental design, not based on the data.

Yes.