explain the following concepts Test of significance, degree of freedom, Hypothesis Testing

Test of Significance: A test of significance is a statistical method that uses sample data to evaluate a hypothesis about a population parameter. The two main types of tests of significance are the parametric tests (t-tests, z-tests, etc.) and the non-parametric tests (Chi-square, etc.). The purpose of a test of significance is to determine the probability that the observed effect in the sample occurred by chance alone, assuming the null hypothesis is true.

Degree of Freedom: Degrees of freedom is a statistical concept that refers to the number of independent pieces of information that are needed to estimate a parameter. In other words, it's the number of values in a study that have the freedom to vary. For example, if you have a sample size of 10, you have 9 degrees of freedom because the last value is determined once you know the first 9.

Hypothesis Testing: Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. It is basically an assumption that we make about the population parameter. Hypothesis testing involves two hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1 or Ha). The null hypothesis is the hypothesis that we assume to be true. The alternative hypothesis is the hypothesis that we want to prove. The process of hypothesis testing involves collecting data, formulating the null and alternative hypothesis, analyzing the data, and then interpreting the result.