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What if a test statistic is more extreme than a null hypothesis?
What if a test statistic is more extreme than a critical value?
What happens if a test statistic is extreme?
Can a null hypothesis be rejected?
How do you test a null hypothesis?
What is a p-value and a null hypothesis?
If the test statistic is more extreme than the critical value, then the null hypothesis is rejected in favor of the alternative hypothesis. If the test statistic is not as extreme as the critical value, then the null hypothesis is not rejected.
- P-Value Approach
The P-value approach involves determining "likely" or...
- S.3.3 Hypothesis Testing Examples
The test statistic t* is 1.22, and the P-value is 0.117. If...
- P-Value Approach
The P-value is the probability of observing a test statistic as extreme (or more extreme) than the one calculated if the null hypothesis were true. In simpler terms, it’s the likelihood of getting your observed results by chance alone.
- Overview
- Statistical Tests
- What does it do?
- When to perform?
- Choosing right one
This article provides information on statistical tests, including when to perform a test, how to choose the right one for your data and what assumptions are made by statistical tests. It also explains the difference between quantitative and categorical variables, as well as discrete and continuous variables.
Statistical tests are used in hypothesis testing to determine whether a predictor variable has a statistically significant relationship with an outcome variable or estimate the difference between two or more groups. They assume a null hypothesis of no relationship and calculate p-value to see if observed data falls outside of predicted range.
A statistical test works by calculating a test statistic that describes how much the relationship differs from the null hypothesis, then calculates p-value which estimates likelihood of observing this difference if there is no real relationship.
You can perform statistical tests on data collected through experiment or probability sampling methods as long as sample size is large enough and meets certain assumptions such as independence, homogeneity, normality etc.
To choose the right one you need to know your data's assumptions and types of variables (quantitative/categorical) being dealt with. Parametric tests have stronger inferences but stricter requirements while nonparametric ones make weaker inferences but don't make many assumptions about data distribution.
The P-value approach involves determining "likely" or "unlikely" by determining the probability — assuming the null hypothesis was true — of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed.
Jul 5, 2024 · If your test statistic is extreme enough, your data are so incompatible with the null hypothesis that you can reject it and conclude that your results are statistically significant. But how does that translate to specific values of your test statistic?
If the test statistic exceeds the critical value, the null hypothesis is rejected. P-values, on the other hand, offer a more flexible approach to decision-making. Instead of a simple yes or no answer, p-values present a range of evidence levels against the null hypothesis.
Jul 17, 2020 · Revised on June 22, 2023. The test statistic is a number calculated from a statistical test of a hypothesis. It shows how closely your observed data match the distribution expected under the null hypothesis of that statistical test.
