## How do you analyze Anova results?

Interpret the key results for One-Way ANOVAStep 1: Determine whether the differences between group means are statistically significant.Step 2: Examine the group means.Step 3: Compare the group means.Step 4: Determine how well the model fits your data.Step 5: Determine whether your model meets the assumptions of the analysis.

## What are the assumptions for a two sample t test?

Two-Sample T-Test AssumptionsThe assumptions of the two-sample t-test are: The data are continuous (not discrete).The data follow the normal probability distribution. The two samples are independent. Both samples are simple random samples from their respective populations.

## What is an example of a parametric test?

Parametric tests assume a normal distribution of values, or a “bell-shaped curve.” For example, height is roughly a normal distribution in that if you were to graph height from a group of people, one would see a typical bell-shaped curve.

## What are the criteria for a parametric test?

Reasons to Use Parametric TestsParametric analysesSample size guidelines for nonnormal data1-sample t testGreater than 202-sample t testEach group should be greater than 15One-Way ANOVAIf you have 2-9 groups, each group should be greater than 15. If you have 10-12 groups, each group should be greater than 20.

## How do you determine if a test is parametric or nonparametric?

If the mean more accurately represents the center of the distribution of your data, and your sample size is large enough, use a parametric test. If the median more accurately represents the center of the distribution of your data, use a nonparametric test even if you have a large sample size.