Statistical Significance
Statistical significance is a determination made by an analyst that the results in the data are not explainable by chance alone. Statistical hypothesis testing is the method by which the analyst makes this determination. This test provides a p-value, which is the probability of observing results as extreme as those in the data, assuming the results are truly due to chance alone. A p-value of 5% or lower is often considered to be statistically significant.
Statistical Significance
Definition: Statistical significance is a tool used by analysts to determine whether data results are not merely due to chance. Through statistical hypothesis testing, analysts can assess the reliability of the results. The test provides a P-value, which indicates the probability of observing the data results as extreme as they are, assuming they are entirely due to chance. Generally, a P-value of 5% or lower is considered statistically significant.
Origin
The concept of statistical significance originated in the early 20th century, introduced by statistician Ronald Fisher. He first introduced the concept of the P-value and set the 5% significance level as a standard in his 1925 book, 'Statistical Methods for Research Workers.'
Categories and Characteristics
Statistical significance mainly falls into two categories: one-tailed tests and two-tailed tests. One-tailed tests are used to detect significance in one direction, while two-tailed tests are used to detect significance in both directions. One-tailed tests have a lower P-value and are easier to achieve significance but are only applicable to hypotheses in a specific direction. Two-tailed tests are more stringent and applicable to hypotheses in any direction.
Specific Cases
Case 1: Suppose a pharmaceutical company claims that its new drug significantly lowers blood pressure. Researchers conduct a trial on 100 patients, and the results show that the average blood pressure in the new drug group decreased by 10mmHg, while the control group only decreased by 2mmHg. Through statistical hypothesis testing, researchers calculate a P-value of 0.03, which is below the 5% significance level, indicating that the drug's effect is statistically significant.
Case 2: In a market survey, a company wants to know if a new advertisement significantly increases product sales. By comparing sales data before and after the advertisement, analysts find a P-value of 0.07, which is above the 5% significance level, so they cannot conclude that the advertisement significantly increased sales.
Common Questions
Question 1: Why choose 5% as the significance level?
Answer: 5% is a traditional standard, indicating a 95% confidence that the result is not due to chance. However, in some fields, stricter or more lenient standards, such as 1% or 10%, may be chosen.
Question 2: Does statistical significance mean practical significance?
Answer: Not necessarily. Statistical significance only indicates that the result is not due to chance, but it does not mean the result is practically important. Practical significance should be judged based on the actual context and effect size.
