Statistical Analysis and Parametric vs. Nonparametric Tests

To draw conclusions about a larger group based on a smaller sample.

Statistical tests that don't rely on specific assumptions about the population's distribution.

Data follows a normal distribution and groups have equal variances.

To understand results and form conclusions about the research question.

Used mainly when you want to analyse categorical data.

Data that is measured on a continuous scale (e.g., height, weight).

Focused on determining if there is evidence to support or reject a specific claim or theory.

Used to test if a single sample's mean is significantly different from a known or hypothesized population mean.

When the data doesn't fit a normal distribution or other assumptions are violated.

Compares the means of two independent groups to see if they are significantly different.

The null hypothesis (H0) states that there is no significant difference between the sample mean and the specified value.

To check if treatment A and B significantly affect depression symptoms differently.

To see if high school grades relate to performance on the college entrance exam.

States there's no initial difference in depression levels between groups before the study.

Each group consists of fifty people.

The Wilcoxon Signed-Rank test.

The Mann-Whitney U test.

There is a significant difference in the mean symptoms of depression between the two groups after treatment.

Each individual is evaluated only once to measure symptom, no repeated measures.

Wilcoxon Signed-Rank Test.

Non-parametric tests do not make assumptions about the population parameters.

Parametric tests make specific assumptions about the population parameters, such as the data being normally distributed.

Parametric test assumes that the data are normally distributed.

One Group