P-value
If [the null is true in context], the probability of obtaining results at least as extreme as the sample is ___.
In statistics, p-value refers to the probability of obtaining a test statistic as extreme as the one observed, assuming the null hypothesis is true. The null hypothesis is the statement being tested in a statistical analysis.
The p-value is used to determine the statistical significance of the test results. If the p-value is less than or equal to the significance level (usually 0.05), the test results are considered statistically significant, and the null hypothesis is rejected.
For example, let’s say we conduct a hypothesis test to determine if the average height of students in a class is greater than 5 feet. Our null hypothesis would be that the average height is 5 feet. We would then collect data and calculate a test statistic, such as a t-statistic or z-score, along with its corresponding p-value. If the p-value is less than or equal to 0.05, we reject the null hypothesis and conclude that the average height of the students is greater than 5 feet.
The p-value is a crucial concept in statistical hypothesis testing and is commonly used in many fields, including biology, economics, psychology, and medicine. It is important to note that just because a result is statistically significant does not mean it is practically significant or meaningful. It is also important to consider other factors, such as effect size and sample size, when interpreting a statistical analysis.
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