Statistical Tests

1-tailed T test

Evaluate the mean of 1 continuous variable.

2 tailed T test

Evaluating the mean of both tails of distribution of continuous variables

Chi-squared test

Compare the observed values in your data to the expected values if the null hypothesis is correct

Fishers exact test

Chi-squared for small sample sizes (fewer than 10 values per cell)

Parametric test

Normally distributed, continuous data

Non-parametric test

Suitable for any continuous data, based on ranks of data values

Pearson’s correlation coefficient

Evaluates the strength and direction of the relationship between 2 continuous variables

ANOVA testing

Used to evaluate the difference between the means of more than 2 groups

Wilcoxon signed rank test

Non-parametric counterpart for t-test.

Kurskal-Wallis test

Non-parametric test to compare 3+ independent groups, extension of Mann Whitney test

Mann Whitney U test

Difference between 2 samples, using rank sums

Spearman’s rank correlation

Non-parametric version of Pearson’s test. Strength and direction of association between 2 ranked variables

Regression

Determine the strength and character of the relationship between variables (dependent and 1+ independent)

Bonferroni correction

The p value of each test must be equal to its alpha divided by the number of tests performed

p value

probability of obtaining observed results or results which are more extreme if the null hypothesis is true