Description: Statistical significance is a determination that a relationship between variables is not due to chance. This concept is fundamental in inferential statistics, where the aim is to establish whether the observed results in a study are robust enough to be considered valid and not merely a product of random variability. To assess statistical significance, statistical tests are used to generate a p-value, which indicates the probability that the observed results are due to chance. A p-value less than a predefined threshold, commonly 0.05, suggests that the observed relationship is statistically significant. However, it is important to note that statistical significance does not necessarily imply that the relationship is of great practical relevance; it is possible for a result to be statistically significant but not have a significant impact in the real world. Therefore, the interpretation of statistical significance should be done with caution, considering the context of the study and the magnitude of the observed effect.
History: The notion of statistical significance was developed in the early 20th century, with the work of statisticians such as Ronald A. Fisher, who introduced the concept of the p-value in his work ‘Statistical Methods for Research Workers’ in 1925. Fisher proposed that a p-value less than 0.05 could be considered a threshold for determining significance. Over the years, this approach has been the subject of debate and revision, particularly regarding its interpretation and the overuse of significance testing without considering effect size or practical relevance.
Uses: Statistical significance is used in various disciplines, including medicine, psychology, social sciences, and economics, to validate hypotheses and determine the effectiveness of treatments or interventions. It is common in clinical trials, where the effect of a new drug is evaluated against a placebo. It is also applied in market studies to analyze the relationship between variables such as advertising and sales.
Examples: An example of statistical significance can be seen in a clinical study evaluating a new drug to reduce blood pressure. If the analysis shows a p-value of 0.03, this would indicate that there is a 3% probability that the observed results are due to chance, suggesting that the drug has a significant effect. Another example could be a study investigating the relationship between study time and academic performance, where a p-value of 0.01 would indicate that the observed correlation is statistically significant.
