What is TRUE about hypothesis testing?

Answer:

(View Below)

Explanation:

True statements about hypothesis testing include:

1. **Hypothesis Testing is a Statistical Method:** Hypothesis testing is a statistical method used to make inferences about populations based on sample data. It involves making educated guesses (hypotheses) about population parameters and using sample data to test these hypotheses.

2. **Two Hypotheses:** In hypothesis testing, there are typically two hypotheses involved:

  - **Null Hypothesis (H0):** This is the default or initial hypothesis. It often represents a statement of no effect, no difference, or no change.

  - **Alternative Hypothesis (Ha or H1):** This is the hypothesis that researchers aim to support. It represents the opposite of the null hypothesis and often indicates an effect, difference, or change.

3. **Significance Level:** Researchers set a significance level (alpha, often denoted as α) before conducting hypothesis testing. This significance level represents the probability of making a Type I error (rejecting a true null hypothesis). Common significance levels are 0.05 (5%) or 0.01 (1%).

4. **Test Statistic:** In hypothesis testing, a test statistic is calculated from the sample data. The choice of test statistic depends on the type of data and the hypothesis being tested.

5. **P-Value:** The p-value is a key result of hypothesis testing. It represents the probability of obtaining the observed results (or more extreme results) under the assumption that the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis.

6. **Comparison with Significance Level:** Researchers compare the calculated p-value with the significance level (α). If the p-value is less than or equal to α, the null hypothesis is rejected in favor of the alternative hypothesis. If the p-value is greater than α, there is insufficient evidence to reject the null hypothesis.

7. **Conclusion:** Based on the comparison of the p-value and the significance level, a conclusion is drawn. If the null hypothesis is rejected, it suggests that the alternative hypothesis is supported. If the null hypothesis is not rejected, there is insufficient evidence to support the alternative hypothesis.

8. **Errors:** Hypothesis testing involves the possibility of two types of errors:

  - **Type I Error:** This occurs when the null hypothesis is rejected when it is actually true (false positive).

  - **Type II Error:** This occurs when the null hypothesis is not rejected when it is actually false (false negative).

9. **Sample Size:** The power of hypothesis testing depends on the sample size. Larger sample sizes are more likely to detect smaller effects or differences if they exist.

10. **Statistical Tests:** There are various statistical tests and methods for hypothesis testing, including t-tests, chi-squared tests, ANOVA, regression analysis, and more. The choice of test depends on the research question and the type of data being analyzed.

11. **Replicability:** Hypothesis testing is an essential part of the scientific method, and research findings