What does statistical significance measure?

Statistical significance primarily measures the probability that an observed effect or difference, such as those between groups in an experiment, could occur due to random chance alone. In other words, it indicates whether the results of an experiment or study reflect true differences or relationships in the population being studied rather than fluctuations that might happen by accident. This probability is often represented by a p-value, with values below a certain threshold (commonly 0.05) indicating that the results are unlikely to have occurred under the null hypothesis, which posits that there is no effect or difference.

Key to understanding this concept is recognizing its importance in hypothesis testing, where researchers assess their data to determine whether to reject the null hypothesis based on statistical evidence. The focus is specifically on quantifying the likelihood of obtaining the observed results if the null hypothesis were true, making it foundational in the interpretation of experimental results and influences in fields such as biomedical engineering.

In the context of the other options, while they may relate to aspects of data analysis or experimental design, they do not specifically capture the essence of what statistical significance aims to convey about the reliability and validity of the observed findings.