Module 12 Practice Questions (2)

Front

What is the Chi-Square test statistic?

Back

The Chi-Square test statistic is a measure used to determine the difference between observed and expected frequencies in categorical data. It is calculated using the formula: χ² = Σ((O - E)² / E), where O is the observed frequency and E is the expected frequency.

Front

How do you calculate the contribution to the Chi-Square test statistic?

Back

The contribution to the Chi-Square test statistic for a single category is calculated as (O - E)² / E, where O is the observed count and E is the expected count.

Front

What does a P-value indicate in hypothesis testing?

Back

A P-value indicates the probability of obtaining the observed results (or more extreme) if the null hypothesis is true. It helps determine the significance of the results.

Front

What are null and alternative hypotheses?

Back

The null hypothesis (H₀) is a statement that there is no effect or no relationship, while the alternative hypothesis (H₁ or Hₐ) is a statement that there is an effect or a relationship.

Front

In hypothesis testing, what does it mean if the null hypothesis is rejected?

Back

If the null hypothesis is rejected, it suggests that there is sufficient evidence to support the alternative hypothesis, indicating a statistically significant effect or relationship.

Front

What is the purpose of a two-way table in statistical analysis?

Back

A two-way table is used to summarize the relationship between two categorical variables, displaying the frequency counts for each combination of categories.

Front

What is the significance of the degrees of freedom in the Chi-Square test?

Back

Degrees of freedom in the Chi-Square test are calculated as (number of rows - 1) * (number of columns - 1). They are used to determine the critical value from the Chi-Square distribution.

Front

What is the difference between a one-tailed and a two-tailed test?

Back

A one-tailed test assesses the probability of an effect in one direction, while a two-tailed test assesses the probability of an effect in both directions.

Front

What is a Type I error in hypothesis testing?

Back

A Type I error occurs when the null hypothesis is incorrectly rejected when it is actually true, leading to a false positive.

Front

What is a Type II error in hypothesis testing?

Back

A Type II error occurs when the null hypothesis is not rejected when it is actually false, leading to a false negative.