Test of significance

A test of significance, often referred to as a statistical significance test, is a method used in statistics to determine whether an observed effect or result is statistically significant or if it could have occurred by chance. This type of test is commonly employed in hypothesis testing to make informed decisions about population parameters based on sample data.
T-Test:

The t-test is used to compare the means of two groups to determine if there is a significant difference between them. There are different variations of the t-test:
Independent Samples T-Test: Compares the means of two independent groups.
Paired Samples T-Test: Compares the means of two related groups.
Z-Test:

Similar to the t-test, the z-test is used to compare means, especially when the population standard deviation is known. It is often used in large sample sizes. There are variations, including the one-sample z-test and the two-sample z-test.
F-Test:

The F-test is commonly used in analysis of variance (ANOVA) to compare the variances of two or more groups. It assesses whether there are statistically significant differences in the variances. In the context of regression, the F-test is used to assess the overall significance of a regression model.
Chi-Square Test:

The Chi-square test is used to assess the association between categorical variables. There are different forms of the chi-square test:
Chi-Square Test for Independence: Examines whether there is a significant association between two categorical variables.
Chi-Square Goodness-of-Fit Test: Tests whether the observed categorical data fits a theoretical distribution.