This category serves as a hub for students, researchers, and beginners who want to learn how to test assumptions and draw meaningful conclusions from data. The tutorials in this category cover hypothesis-testing related topics such as null and alternative hypothesis concepts, selecting appropriate tests, calculating test statistics, and interpreting p-values and confidence intervals.
The category also contains tutorials on commonly used statistical tests such as t-tests, chi-square tests, ANOVA, z-tests, and more. Each of the tutorials in this category is clear and step-by-step for beginners. Therefore, by using our hypothesis testing tutorials, you’ll not only understand the calculations but also the reasoning behind each method. This will consequently make it easier for you to apply these important concepts in real-world and academic projects.
A one-proportion z-test is one of the hypothesis tests used to determine whether a sample proportion is significantly different from a hypothesized population proportion. It is suitable when the outcome has two…
When performing a hypothesis test for a sample mean where the population standard deviation is known, or when performing one-proportion or two-proportion z-tests, you will often come across the z test statistic.…
The t-critical value is an essential part of statistics and hypothesis testing. It helps you decide whether your results are significant or just due to chance. Without the t-critical value, it is…
A z-critical value is a cutoff point on a standard normal distribution (z-distribution) used in hypothesis testing to determine whether the results are statistically significant or not. The value is compared with…