Hypothesis TestingTest of significance (a level)Degree of freedom (df)One Tailed and Two Tailed TestType I and Type II Errors

Sure, here are the explanations for the terms you've mentioned:

1. Hypothesis Testing: This 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.

2. Test of significance (a level): The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.

3. Degree of freedom (df): Degrees of freedom of an estimate is the number of independent pieces of information that went into calculating the estimate. It’s often used in formulas to calculate statistics that are based on samples, such as the t-distribution.

4. One Tailed and Two Tailed Test: A test of a statistical hypothesis, where the region of rejection is on only one side of the sampling distribution, is called a one-tailed test. On the other hand, a two-tailed test is a statistical test in which the critical area of a distribution is two-sided and tests whether a sample is greater than or less than a certain range of values.

5. Type I and Type II Errors: Type I error is the rejection of a true null hypothesis (also known as a "false positive" finding), while a type II error is failing to reject a false null hypothesis (also known as a "false negative" finding).