What are the main steps involved in conducting a hypothesis test in statistics?
Hypothesis testing involves forming hypotheses, calculating a test statistic and p-value, comparing to significance level, and concluding accordingly.
What is the first step in conducting a hypothesis test in statistics?
The first step is to write the null hypothesis (H0) and the alternative hypothesis (Ha) using mathematical symbols based on the problem statement.
How is the null hypothesis (H0) typically written in terms of symbols and values?
The null hypothesis is written as a population parameter (like μ, p, or σ) equals a specific value, such as μ = 23 or p = 0.2.
What does the alternative hypothesis (Ha) represent in a hypothesis test?
The alternative hypothesis represents the claim you are trying to find evidence for, using the same parameter and value as H0 but with a different symbol (≠, <, or >).
What is a test statistic and how is it used in hypothesis testing?
A test statistic, such as a z-score or t-score, measures how different the sample data is from the value stated in the null hypothesis.
When do you use a z-score versus a t-score as your test statistic?
You use a z-score when the population standard deviation (σ) is known, and a t-score when it is unknown.
What is a p-value in the context of hypothesis testing?
A p-value is the probability of obtaining a sample statistic as extreme as the one observed, assuming the null hypothesis is true.
How do you determine whether to reject or fail to reject the null hypothesis using the p-value?
You compare the p-value to the significance level (alpha); if the p-value is less than alpha, you reject the null hypothesis, otherwise you fail to reject it.
What is the significance level (alpha) and how is it used in hypothesis testing?
The significance level (alpha) is a threshold probability, commonly 0.05, that defines how unlikely a sample result must be to reject the null hypothesis.
How should you state the final conclusion of a hypothesis test in plain English?
You should state whether there is enough evidence or not enough evidence to support the alternative hypothesis, based on whether you rejected or failed to reject the null hypothesis.