Hypothesis Testing
Hypothesis testing is a formal statistical method used to decide whether sample data provides enough evidence to reject a claim about a population.
What You Need to Know
Key Concept Diagram
The null hypothesis H₀ assumes no effect or no difference; the alternative hypothesis H₁ proposes a change
The p-value is the probability of observing results at least as extreme as the sample if H₀ is true
A significance level α (commonly 0.05) sets the threshold below which we reject H₀
Type I error is rejecting a true H₀; Type II error is failing to reject a false H₀
Key Vocabulary
Null Hypothesis
The default assumption that there is no effect or no difference in the population
p-value
The probability, assuming the null hypothesis is true, of obtaining a result as extreme as the observed data
Significance Level
The pre-chosen probability threshold α below which the null hypothesis is rejected
Test Statistic
A numerical value calculated from sample data used to decide whether to reject the null hypothesis
Knowledge Check
Select the correct answer for each question. Click "Check Answer" to see if you are right.
Question 1
A researcher sets α = 0.05 and obtains a p-value of 0.03. What conclusion should be drawn?
Question 2
What is a Type I error?
Question 3
The null hypothesis in a study is "the drug has no effect". Which statement correctly describes H₁?
Key Concepts Summary
- ●The null hypothesis H₀ assumes no effect or no difference; the alternative hypothesis H₁ proposes a change
- ●The p-value is the probability of observing results at least as extreme as the sample if H₀ is true
- ●A significance level α (commonly 0.05) sets the threshold below which we reject H₀
- ●Type I error is rejecting a true H₀; Type II error is failing to reject a false H₀