Step 1: Understand the chi-square test.
The chi-square test is a statistical test used to determine if there is a significant association between observed and expected frequencies. It is a non-parametric test because it does not assume a normal distribution of the data.
Step 2: Analyze each option.
- (A) It is a non-parametric test: Correct. The chi-square test does not assume any underlying distribution of the data, making it a non-parametric test.
- (B) It is based on frequencies and not on parameters like mean and standard deviation: Correct. The chi-square test works with frequency data (observed vs. expected counts) and does not involve parameters such as mean and standard deviation.
- (C) It can be used when individual observations of the sample are dependent: Incorrect. The chi-square test assumes that the observations are independent. It is not appropriate for dependent observations.
- (D) It can be used for testing hypothesis but is not useful for estimation: Correct. The chi-square test is primarily used for hypothesis testing, especially for categorical data, but it does not provide parameter estimates.
Step 3: Conclusion.
The correct answer is (A), (B) and (C) only. The chi-square test is non-parametric, based on frequencies, and is used for hypothesis testing.