Statistical Significance
Statistical significance is a concept in statistics that indicates whether the results of an experiment or study are likely due to a real effect rather than random chance. It is typically assessed using p-values, confidence intervals, or hypothesis testing, with a common threshold (e.g., p < 0.05) to determine if findings are statistically significant. This concept is fundamental in research, data analysis, and decision-making to validate hypotheses and draw reliable conclusions.
Developers should learn statistical significance when working with data-driven applications, A/B testing, machine learning model evaluation, or any scenario involving data analysis to ensure results are meaningful and not artifacts of randomness. For example, in software development, it helps validate the effectiveness of new features, optimize algorithms, or assess user behavior changes, preventing false positives and supporting evidence-based decisions.