The null hypothesis assumes that there is no significant difference or relationship between the variables being studied.

The alternative hypothesis proposes the existence of a significant difference or relationship between the variables being studied.

The significance level represents the risk of rejecting the null hypothesis when it is actually true, leading to a Type I error.

A Type I error involves rejecting a true null hypothesis, leading to an incorrect conclusion.

A Type II error involves accepting a false null hypothesis, leading to an incorrect conclusion.

The power of a statistical test represents the probability of correctly rejecting a false null hypothesis, avoiding a Type II error.

The Chi-square test is a non-parametric statistical test that is used to determine whether there is a significant relationship between two categorical variables.

The t-test is a parametric statistical test that is used to compare the means of two groups.

The F-statistic in ANOVA is used to test the hypothesis that the means of two or more groups are equal.

The coefficient of determination (R²) in regression analysis represents the proportion of variance in the dependent variable that is explained by the independent variables.

The mean is a measure of central tendency that represents the average value of a set of data.

The range is a measure of dispersion that represents the difference between the maximum and minimum values in a set of data.

The standard deviation is a measure of dispersion that represents the amount of variation in a set of data.

The normal distribution is also known as the Gaussian distribution, the bell curve, and the symmetric distribution.

The central limit theorem states that the distribution of sample means will be approximately normal, regardless of the shape of the population distribution.