**Statistical significance**: Significance that persists even after eliminating ‘chance’.

**Level of significance**: An indicator/ measure of the magnitude of statistical significance, this is also called ‘alpha’. Typical values range from 5% (which corresponds to a p value of 0.05) to 0.01% (p value of 0.0001).

**Test of statistical significance**: A test that helps compare one’s finding(s) with some other value(s) in order to determine if statistical significance exists at a given level of significance.

**Parametric tests (of significance)**: Tests that assume the values are normally distributed. Example: *student’s t test*

**Non-parametric tests (of significance)**: Tests that do not require/ assume the data to be normally distributed. Example:** Mann-Whitney U test.**

**Hypothesis testing**: A way of checking if a given statement may be true.

**Null Hypothesis**: A statement that says there is no difference between two alternatives.

**Alternative Hypothesis**: A statement that says there is a difference between the alternatives.

**Two-sided hypothesis**: A statement that does not attempt to predict which alternative is better or worse; merely says the two alternatives are not equal/ same.

**One-sided hypothesis**: A statement that clearly says one alternative is better, indicating which way the results might be expected to lean.

**Null Hypothesis Significance Testing**: A specific way of testing a statement. It involves stating the Null Hypothesis

**p value**: The probability of obtaining similar or more extreme results if the null hypothesis is true.

**Clinical significance**: Distinct from statistical significance, this indicates the clinical (real-world) importance of a finding. Statistically significant findings may be clinically irrelevant, and vice-versa.

### Like this:

Like Loading...