Two-Sample Statistical Hypothesis Testing Task: Difference between revisions
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A [[Two-Sample Statistical Hypothesis Testing Task]] is a [[statistical hypothesis testing task]] with a [[two-sample hypothesis]] | A [[Two-Sample Statistical Hypothesis Testing Task]] is a [[statistical hypothesis testing task]] with a [[two-sample hypothesis]] (on 2 [[statistical sample]]s). | ||
* <B>See:</B> [[Welch's Test]], [[Two-Tailed Test]], [[Independent Two-Sample t-Test]]. | * <B>See:</B> [[Welch's Test]], [[Two-Tailed Test]], [[Independent Two-Sample t-Test]]. | ||
Latest revision as of 01:16, 1 March 2023
A Two-Sample Statistical Hypothesis Testing Task is a statistical hypothesis testing task with a two-sample hypothesis (on 2 statistical samples).
References
- http://org.elon.edu/econ/sac/twosample.htm
- QUOTE: Two-sample hypothesis testing is statistical analysis designed to test if there is a difference between two means from two different populations. For example, a two-sample hypothesis could be used to test if there is a difference in the mean salary between male and female doctors in the New York City area. A two-sample hypothesis test could also be used to test if the mean number of defective parts produced using assembly line A is greater than the mean number of defective parts produced using assembly line B. Similar to one-sample hypothesis tests, a one-tailed or two-tailed test of the null hypothesis can be performed in two-sample hypothesis testing as well. The two-sample hypothesis test of no difference between the mean salaries of male and female doctors in the New York City area is an example of a two-tailed test. The test of whether or not the mean number of defective parts produced on assembly line A is greater than the mean number of defective parts produced on assembly line B is an example of a one-tailed test.