What threshold p value would you use to ascribe significant to the results of your inferential statistics, briefly explain your reasoning

## P-value Threshold in Inferential StatisticsThe threshold p-value, also known as the significance level, is a predetermined value that helps us decide whether to reject the null hypothesis in statistical testing. The most commonly used p-value threshold is **0.05**. ### Reasoning Behind Using 0.05 as the ThresholdThe p-value of 0.05 is a convention that has been widely adopted in many fields of research. Here's why:1. **Balance between Type I and Type II errors**: A p-value of 0.05 strikes a balance between avoiding Type I errors (false positives, or rejecting a true null hypothesis) and Type II errors (false negatives, or failing to reject a false null hypothesis). 2. **Tradition and convenience**: The 0.05 level is a tradition that dates back to R.A. Fisher, a pioneer in the field of statistics. It's a convenient choice that has stood the test of time.3. **Practical significance**: A p-value less than 0.05 indicates that the observed data would be quite unlikely if the null hypothesis were true. This gives us a practical benchmark for declaring a result "statistically significant."### When to Use a Different ThresholdWhile 0.05 is the most common threshold, it's not always the best choice. In some cases, you might want to use a more stringent threshold (like 0.01) or a more lenient one (like 0.10). - **More stringent (e.g., 0.01)**: If a Type I error would have serious consequences, you might want to use a lower p-value to reduce the risk of a false positive. - **More lenient (e.g., 0.10)**: If it's more important to avoid Type II errors, you might use a higher p-value to increase the chance of detecting a true effect.Remember, the choice of p-value threshold should be guided by the specific context and objectives of your research. It's also important to consider the size of the effect and the quality of the data, not just the p-value.