In hypothesis testing, the Null Hypothesis (H₀) typically represents ___ and always contains an equality.

No effect, no difference, or no change from a known value.

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

One-tailed tests focus on direction.

• One-tailed looks for specific changes.

• Two-tailed tests any direction difference.

• Choice depends on hypothesis type.

Fill in the blank: The significance level, denoted by α, represents the probability of ___ when the null hypothesis is actually true.

Rejecting a true null hypothesis (Type I error).

True or False: A p-value greater than the significance level α indicates strong evidence against the null hypothesis.

False. A p-value greater than α indicates weak evidence against the null hypothesis.

What is a Type II error in the context of hypothesis testing?

Failing to reject a false null hypothesis, which is a missed detection.

Riddle: I am a statistical measure that helps you decide whether to reject a claim based on sample data. What am I?

A significance test.

In hypothesis testing, what does the test statistic measure?

How far our sample result is from what the null hypothesis predicts.

Fill in the blank: The power of a test is defined as ___, representing the probability of correctly rejecting a false null hypothesis.

1 - β.

What determines whether we use a z-test statistic or a t-test statistic?

Z-test vs. T-test Determination

• Z-test: known population standard deviation.

• T-test: unknown, estimate from sample.

• Choose test based on data context.

Explain the relationship between statistical significance and practical importance in hypothesis testing.

Statistical significance doesn't guarantee practical relevance.

• P-value indicates chance likelihood.

• Statistical results can be misleading.

• Importance varies in real-world applications.