Test Statistics <3

Question

Answer

What is the purpose of the One-Sample t-Test?

To test if the mean of a single sample differs from a known or hypothesized population mean.

What are the assumptions of the One-Sample t-Test?

The sample is randomly selected, and the population from which the sample is drawn is normally distributed.

What keywords indicate the use of a One-Sample t-Test?

When there is one sample and a known population mean, testing against a specific value (e.g., 140 mmHg for blood pressure).

How do you interpret the results of a One-Sample t-Test?

Reject the null hypothesis if the p-value < α (significance level), indicating a significant difference.

What is the purpose of the Two-Sample t-Test (Independent Samples)?

To compare the means of two independent groups.

What are the assumptions of the Two-Sample t-Test (Independent Samples)?

The two samples are independent, both populations are normally distributed, and there is homogeneity of variances (if not met, use Welch's t-test).

What keywords indicate the use of a Two-Sample t-Test (Independent Samples)?

Two separate groups being compared (e.g., different teaching styles, drug treatments).

How do you interpret the results of a Two-Sample t-Test (Independent Samples)?

Reject the null hypothesis if the p-value < α, indicating a significant difference between the two group means.

What is the purpose of the Paired-Sample t-Test?

To compare means from the same group at two different times (or under two different conditions).

What are the assumptions of the Paired-Sample t-Test?

The samples are paired (related) and the differences are normally distributed.

What keywords indicate the use of a Paired-Sample t-Test?

Pre-test and post-test scenarios, before and after treatment conditions.

How do you interpret the results of a Paired-Sample t-Test?

Reject the null hypothesis if the p-value < α, indicating a significant difference in means over time or condition.

What is the purpose of ANOVA (Analysis of Variance)?

To compare the means of three or more groups.

What are the assumptions of ANOVA (Analysis of Variance)?

Independence of observations, normality of the populations, homogeneity of variances.

What keywords indicate the use of ANOVA (Analysis of Variance)?

More than two groups being compared, testing if all group means are equal.

How do you interpret the results of ANOVA (Analysis of Variance)?

Reject the null hypothesis if the p-value < α, indicating at least one group mean is different.

What is the purpose of the Chi-Square Test?

To test the association between categorical variables or the goodness of fit.

What are the assumptions of the Chi-Square Test?

The data are counts or frequencies, the samples are independent, and the expected frequency in each category should be at least 5.

What keywords indicate the use of a Chi-Square Test?

Categorical data, contingency tables, testing independence or goodness of fit.

How do you interpret the results of a Chi-Square Test?

Reject the null hypothesis if the p-value < α, indicating a significant association or a difference from the expected distribution.

What is the purpose of the Kruskal-Wallis Test?

A non-parametric method for comparing more than two independent groups.

What are the assumptions of the Kruskal-Wallis Test?

Data do not need to be normally distributed, data should be independent and ordinal or continuous.

What keywords indicate the use of the Kruskal-Wallis Test?

Non-normal data, more than two groups, use when ANOVA assumptions are violated.

How do you interpret the results of the Kruskal-Wallis Test?

Reject the null hypothesis if the p-value < α, indicating at least one group distribution differs.

What are the general steps for hypothesis testing?

State the hypotheses, select the appropriate test, calculate the test statistic, determine the p-value or critical value, make a decision, draw a conclusion.

What should you consider regarding assumptions checking in hypothesis testing?

Always verify if the assumptions of the test are met (e.g., normality, independence).

Why is sample size and power important in hypothesis testing?

Consider the size and power of the test, especially in small samples.

How does the type of data affect the choice of statistical test?

Ensure the test is suitable for the data type (categorical, continuous).