Hypothesis Testing Overview

These flashcards cover essential concepts related to hypothesis testing, including definitions, types of errors, and the reasoning behind using samples.

What is a population in hypothesis testing?

The entire group we care about, such as all adults with anxiety.

What is a sample in hypothesis testing?

The smaller group we actually study, like 30 patients in a trial.

Why do researchers use samples instead of populations?

We rarely measure whole populations and samples provide only imperfect estimates.

What does μ represent in hypothesis testing?

Population mean (true average).

What does σ represent in hypothesis testing?

Population standard deviation (true spread).

Why do we divide by n - 1 for sample variance?

Because samples tend to underestimate population variability; dividing by n - 1 makes the estimate unbiased.

What is the null hypothesis (H₀)?

The presumption that there is no effect or no difference.

What is the alternative hypothesis (H₁)?

The presumption that there is an effect or difference.

What does a p-value represent?

The probability of obtaining a result at least as extreme as the observed result under the null hypothesis.

What is a Type I error in hypothesis testing?

Rejecting the null hypothesis when it is actually true.

What is a Type II error in hypothesis testing?

Failing to reject the null hypothesis when it is actually false.

What is the purpose of a z-score in hypothesis testing?

It standardizes scores to assess how far a value is from the mean in terms of standard deviations.

Why is it necessary to follow a structured process in hypothesis testing?

To ensure decisions are made fairly and logically based on statistical evidence.

What does α represent in hypothesis testing?

The alpha level is the threshold for statistical significance, commonly set at 0.05.

What is the critical value in hypothesis testing?

A z-score that marks the boundary of the 'unusual' or 'rare event' zone.

How do you determine whether to reject the null hypothesis?

By comparing the p-value against the alpha level.

What is meant by normal distribution in the context of hypothesis testing?

Scores should roughly produce a bell-shaped curve for the test to work effectively.

What is the importance of random sampling in hypothesis testing?

To ensure that the sample accurately represents the population.

What does the decision rule in hypothesis testing entail?

It sets the standard for determining whether the evidence is strong enough to reject the null hypothesis.