Julius Ndung’u - Statistical tests

Chi Square Test

The Chi-Square test is a non-parametric statistical technique used to examine the association between categorical variables. It is widely used in various fields, from social sciences to medical research, to test hypotheses about relationships between categorical data.

Correlation Test

Correlation is a statistical measure that describes the extent to which two variables change together. In other words, it quantifies the degree of relationship or association between two continuous variables. Correlation does not imply causation, but it indicates the strength and direction of a linear relationship between variables.

McNemar’s test

McNemar’s test is a non-parametric statistical test used to analyze paired nominal data. It is particularly useful for assessing changes in responses for matched pairs or before-and-after studies.

One Sample T test

Understanding the One-Sample t-Test: A Comprehensive Guide with R Examples

One Way Anova

Analysis of Variance (ANOVA) is a powerful statistical technique used to compare means across multiple groups. One of the most commonly used forms of ANOVA is the one-way ANOVA, which helps determine whether there are statistically significant differences between the means of three or more independent groups. In this article, we will explore the concept of one-way ANOVA, discuss its importance, and provide a detailed example using R.

Repeated Measure Anova

Repeated Measures ANOVA (Analysis of Variance) is a statistical technique used when the same subjects are measured multiple times under different conditions. This method is particularly useful in experimental designs where the same participants are exposed to various treatments or conditions.

Two Sample T test

The two-sample t-test is a fundamental tool used to determine if there is a significant difference between the means of two independent groups. This test is widely used in various fields, from medical research to business analytics, to compare different groups and draw meaningful conclusions.