What statistical measure indicates the likelihood of random chance influencing results?

The p-value is a statistical measure that indicates the likelihood of random chance influencing the results of an experiment or study. It helps researchers determine the strength of the evidence against the null hypothesis, which posits that there is no effect or no difference. A lower p-value suggests that the observed results are less likely to have occurred by random chance, leading to the conclusion that there may indeed be a statistically significant effect or relationship present.

In scientific research, a commonly accepted threshold for significance is a p-value of less than 0.05. This threshold indicates that there is less than a 5% probability that the results observed could be due to random chance alone. Therefore, the p-value plays a crucial role in hypothesis testing, guiding researchers in making decisions about whether to reject the null hypothesis based on the observed data.

Other statistical measures, while important in their own right, do not directly address the likelihood of random chance affecting the results. For example, standard deviation measures the amount of variation or dispersion in a set of values, but it does not indicate probability. The correlation coefficient assesses the strength and direction of a relationship between two variables but does not quantify the chance of random occurrence. The Z-score signifies how many standard deviations an element is from the mean