## How do you calculate the test statistic?

Generally, the test statistic is calculated as the pattern in your data (i.e. the correlation between variables or difference between groups) divided by the variance in the data (i.e. the standard deviation).

## What does the test statistic tell you?

The test statistic tells you how different two or more groups are from the overall population mean, or how different a linear slope is from the slope predicted by a null hypothesis. Different test statistics are used in different statistical tests.

What is a test statistic number?

A test statistic is a number calculated by a statistical test. It describes how far your observed data is from the null hypothesis of no relationship between variables or no difference among sample groups.

### What test statistic do I use?

Choosing a nonparametric test

Predictor variable Use in place of…
Chi square test of independence Categorical Pearson’s r
Sign test Categorical One-sample t-test
Kruskal–Wallis H Categorical 3 or more groups ANOVA
ANOSIM Categorical 3 or more groups MANOVA

### Can a test statistic be greater than 1?

As the answer explains, P-values are probabilities and so cannot exceed 1, so whatever argument you had in mind was fallacious.

How do you interpret the p-value and test statistic?

The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.

1. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.
2. A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.

## Is the test statistic the p-value?

The p-value is a number, calculated from a statistical test, that describes how likely you are to have found a particular set of observations if the null hypothesis were true. P-values are used in hypothesis testing to help decide whether to reject the null hypothesis.

## How do you find the p-value of a test statistic?

How to calculate p-value from test statistic?

1. Left-tailed test: p-value = cdf(x)
2. Right-tailed test: p-value = 1 – cdf(x)
3. Two-tailed test: p-value = 2 * min{cdf(x) , 1 – cdf(x)}