Test

Test Purpose

Assumptions

1

Parametric Test Type

1.1

t-Test

1.1.1

One-Sample t-Test

Tests whether there is a difference between the mean of a selected sample and the mean of a known or hypothesized pop ulation.

Normally distributed data, interval or ratio scale, sample size generally > 30.

1.1.2

Independent (Two-Sample) t-Test

Make a comparison between the means of two independent groups to justify there is a big different.

Normally distributed data in each group, equal variances (or use of Welch's correction if variances are unequal), independent samples.

1.1.3

Paired Sample t-Test (Dependent t-Test)

Make a comparison between the means of two related groups (e.g., the same group measured at two different times).

Normally distributed differences, interval or ratio data, paired samples

1.2

ANOVA

It is an important statistical approach used to compare the means of data for three or more groups with the aim of determining whether these differences have a statistical explanation.

Try to compares the means of more than two groups

1.2.1

One-Way ANOVA

1.2.2

Two-Way ANOVA

1.2.3

Repeated Measures ANOVA

1.3

Pearson Correlation

The Pearson correlation coefficient is widely used to measure the strength and direction of a linear relationship between two continuous variables and is therefore useful for biological researchers to evaluate all associations.

Linearity, Continuous Data, Normality, Homogeneity of Variances

1.4

Linear Regression

It is a statistical approach that is considered one of the fundamental and widely used methods in biostatistics and is considered a very important method in analyzing and frameworking the relationship between the dependent and independent variables

Linearity, Independence, Normality of Residuals, Homoscedasticity

1.4.1

Simple

One independent variable with one depended variable

1.4.2

Multiple

One depended variable with more than one independent variables