Hypothesis Testing
2024 | AI Dictionary
What is Hypothesis Testing: A statistical method for making inferences about a population using sample data, evaluating null and alternative hypotheses.
What is Hypothesis Testing?
Hypothesis Testing is a statistical method used to make inferences or draw conclusions about a population based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1), then determining whether the sample data supports or rejects the null hypothesis.
Types of Hypothesis Tests
- Z-test: Used when the sample size is large and the population variance is known.
- T-test: Used when the sample size is small and the population variance is unknown.
- Chi-square test: Used to determine whether there is a significant association between categorical variables.
Applications of Hypothesis Testing
- A/B Testing: Used to compare two versions of a webpage or app to determine which performs better.
- Drug Testing: Helps determine whether a new drug is effective compared to a placebo.
- Quality Control: Assesses whether a manufacturing process produces products that meet certain specifications.
Example of Hypothesis Testing
In a clinical trial testing the effectiveness of a new medication, the null hypothesis might state that the medication has no effect on patients, while the alternative hypothesis could state that the medication has a positive effect. By analyzing the trial data, researchers can determine if the evidence supports rejecting the null hypothesis and accepting the alternative.
Did you liked the Hypothesis Testing gist?
Learn about 250+ need-to-know artificial intelligence terms in the AI Dictionary.