Hypothesis Testing
Definition
Hypothesis testing is a statistical method used to make inferences about population parameters based on sample data. It involves formulating a null hypothesis and an alternative hypothesis, collecting data, calculating test statistics, and making decisions about rejecting or failing to reject the null hypothesis.
Analogy
Imagine you're a detective trying to solve a crime. You have two hypotheses - one that states the suspect is innocent (null hypothesis) and another that states the suspect is guilty (alternative hypothesis). You collect evidence (data) and analyze it using statistical methods (calculating test statistics). Based on your analysis, you make a decision about whether there's enough evidence to convict or not (reject or fail to reject the null hypothesis).
Related terms
Type I Error: A type I error occurs when we reject the null hypothesis when it is actually true.
Type II Error: A type II error occurs when we fail to reject the null hypothesis when it is actually false.
One-Tailed Test: In a one-tailed test, we are only interested in detecting an effect in one direction (e.g., greater than or less than).
"Hypothesis Testing" appears in:
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Practice Questions (5)
- What is the significance level commonly used in hypothesis testing?
- How does a larger t-score affect the decision in hypothesis testing?
- In hypothesis testing, if the significance level is set at 0.05, what is the probability of a Type I error?
- What is the significance level in hypothesis testing?
- What does it mean if the p-value is less than the level of significance in hypothesis testing?
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