The correct answer to the statement "Used to determine whether observed sample frequencies differ significantly from expected frequencies" is:

Chi Square Test for Goodness of Fit

Explanation:

• Chi-Square Test for Goodness of Fit: This test is used to determine whether the distribution of sample frequencies matches the expected frequencies. It compares the observed frequency distribution with an expected distribution to see if any significant differences exist. It is often used for categorical data.

• Wilcoxon Test: This is a non-parametric test used to compare two related samples or repeated measurements on a single sample to assess whether their population mean ranks differ. It is not used for comparing frequencies.

• t-test for Dependent Means: Also known as the paired t-test, it is used to compare the means of two related groups, typically before and after treatment, not to compare frequencies.

• Chi-Square Test for Independence: This test is used to determine whether two categorical variables are independent of each other, not to compare observed and expected frequencies in a single variable.

Summary:

• The Chi-Square Test for Goodness of Fit is the correct test for comparing observed sample frequencies to expected frequencies.

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Would you like more details or clarification on these tests?

Here are some related questions for further exploration:

1. What are the assumptions of the Chi-Square Test for Goodness of Fit?
2. How is the Chi-Square statistic calculated?
3. When would you use the t-test for dependent means over the Wilcoxon test?
4. What is the difference between the Chi-Square Test for Goodness of Fit and the Chi-Square Test for Independence?
5. How do you interpret the p-value in a Chi-Square Test for Goodness of Fit?

Tip: For a Chi-Square test, make sure your expected frequency for each category is at least 5 to ensure the validity of the test.