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WorksheetsHypothesis Testing Fundamentals
Total questions: 13
Worksheet time: 10mins
What is the primary purpose of a two sample t-test?
To test the hypothesis of a population proportion.
To determine the correlation between two variables.
To analyze the variance within a single group.
To compare the means of two independent groups.
When would you use a one variance test?
You would use a one variance test when testing if the variance of a single population is equal to a specific value.
When comparing the variances of two different populations.
When testing the mean of a single population.
When assessing the correlation between two variables.
What is the main difference between parametric and non-parametric tests?
Non-parametric tests require a specific distribution; parametric tests do not.
Parametric tests can only be used with small sample sizes, while non-parametric tests can be used with large samples.
Parametric tests assume a specific distribution; non-parametric tests do not.
Parametric tests are always more accurate than non-parametric tests.
Explain the concept of a paired t-test.
A paired t-test compares the means of two related groups to determine if there is a statistically significant difference between them.
A paired t-test compares the means of two independent groups.
A paired t-test is used to analyze variance in a single group.
A paired t-test determines the correlation between two variables.
What assumptions must be met to perform a two sample Pooled t-test?
Independence of samples, normality of data, and equal variances.
Samples must be paired
Variances must be unequal
Data must be categorical
How do you determine if a chi-square test for association is appropriate?
Data must be continuous
Observations can be dependent
Check if the data is categorical, sample size is large enough, observations are independent, and variables are nominal or ordinal.
Sample size can be very small
What is the null hypothesis in a one variance test?
H0: σ² = σ0²
H0: σ² < σ0²
H0: σ² ≠ σ0²
H0: σ² > σ0²
Describe the conditions under which a two sample z-test can be used.
A two sample z-test can be used when comparing means of two independent samples with known population variances or large sample sizes.
A two sample z-test can be used to compare proportions of two related groups.
A two sample z-test is appropriate for small sample sizes regardless of variance knowledge.
A two sample z-test can be used for dependent samples with unknown variances.
What does it mean if a test is non-parametric?
A test is non-parametric if it can only be used for categorical data.
A test is non-parametric if it requires a normal distribution for the data.
A test is non-parametric if it assumes equal variances among groups.
A test is non-parametric if it does not assume a specific distribution for the data.
What is the significance level in hypothesis testing?
The significance level is the threshold for determining the sample size in a study.
The significance level indicates the strength of the alternative hypothesis.
The significance level in hypothesis testing is the probability of rejecting the null hypothesis when it is true.
The significance level is the probability of accepting the null hypothesis when it is false.
Explain the difference between independent and dependent samples in hypothesis testing.
Independent samples are always paired groups.
Independent samples are always larger than dependent samples.
Dependent samples are completely unrelated groups.
Independent samples are unrelated groups; dependent samples are related or paired groups.
What is the role of normality tests in hypothesis testing?
Normality tests help determine if the assumption of normality is met for valid hypothesis testing.
Normality tests are only applicable to categorical data.
Normality tests are used to calculate p-values directly.
Normality tests determine the sample size needed for testing.
How do you interpret the results of a goodness of fit test?
Analyze the mean and median of the data set.
Use a chi-square test to find the correlation between variables.
Visualize the data with a histogram to check for normality.
Compare the p-value to the significance level to determine if the data fits the expected distribution.
