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There could be several reasons why your p-value or rejection region may be incorrect. Some common reasons include incorrect calculation, using the wrong statistical test, or not considering all relevant variables in your analysis.
You can check the accuracy of your p-value or rejection region by reviewing your calculations and ensuring that you have used the correct statistical test for your data. It is also helpful to consult with a statistician or conduct a peer review of your analysis.
Yes, a small sample size can impact the accuracy of your p-value or rejection region. With a smaller sample size, there is a higher likelihood of random chance influencing your results, leading to an incorrect p-value or rejection region.
To avoid incorrect p-values or rejection regions, it is important to carefully plan and design your study, ensuring that you have an appropriate sample size and use the correct statistical tests. It is also important to thoroughly review your calculations and seek feedback from other experts in the field.
Yes, some common mistakes that can result in incorrect p-values or rejection regions include using the wrong statistical test, not considering all relevant variables, and making errors in calculations. It is also important to be aware of biases and confounding factors that may influence your results.