In statistics, p value refers to the probability of obtaining sample data or more extreme cases under zero hypothesis. The significance level of 10% means that there is a 10% possibility to accept a wrong null hypothesis, that is, to make the first kind of mistakes. And the p value is an index used to judge whether the first kind of mistakes have been made. If the p value is less than the significance level, usually 0.05 or 0.0 1, the null hypothesis is rejected and the sample data is considered inconsistent with the null hypothesis.
Taking the significance level of 10% as an example, if the p value is less than or equal to 0. 1, it can be considered that the sample data is inconsistent with the original hypothesis and the original hypothesis is rejected. If the p value is greater than 0. 1, the null hypothesis cannot be rejected and the sample data is considered to be consistent with the null hypothesis.
When conducting hypothesis testing, it is necessary to choose the significance level according to the specific situation. There are three common types: 0.05, 0.0 1, 0. 1. Choosing significance level needs to consider the purpose and needs of actual research, as well as the types and costs of possible errors. In practical application, 0.05 or 0.0 1 is usually chosen as the significance level.
In hypothesis testing, if the p value is found to be very small, such as less than 0.05 or 0.0 1, the sample data can be considered inconsistent with the original hypothesis, and the original hypothesis can be rejected, and the sample data is considered statistically significant. However, if the p value is large, such as more than 0. 1, the null hypothesis cannot be rejected, and the sample data is consistent with the null hypothesis, so no meaningful conclusion can be drawn.
In short, at the significance level of 10%, the p value is 0. 1, which is an index to measure the consistency between the sample data and the original hypothesis. Choosing significance level needs to consider the purpose and needs of actual research, as well as the types and costs of possible errors.