What's the significance of statistical significance in experimental results?

Statistical significance indicates the likelihood that the results of an experiment are not due to chance.

Statistical significance is a crucial concept in experimental psychology and other scientific disciplines. It provides a measure of the probability that the observed difference between groups in an experiment occurred by chance. In other words, it helps to determine whether the results of an experiment are 'real' or if they could have happened randomly.

When conducting an experiment, researchers often use a statistical test to compare their results to what would be expected by chance. This test provides a p-value, which is the probability of obtaining the observed results (or more extreme) if the null hypothesis (i.e., there is no effect) is true. If this p-value is less than a predetermined threshold (commonly 0.05), the results are deemed statistically significant. This means that there is less than a 5% chance that the observed results occurred by chance, assuming the null hypothesis is true.

Statistical significance is important because it gives researchers confidence in their findings. If an experiment's results are statistically significant, it suggests that the observed effect is likely to be a true effect, not just a random occurrence. This can provide strong evidence to support a hypothesis or theory.

However, it's important to remember that statistical significance does not necessarily mean practical or clinical significance. An effect can be statistically significant but still be of little practical importance. For example, a drug might significantly reduce symptoms compared to a placebo, but the actual reduction might be so small that it doesn't make a meaningful difference to patients' lives.

Moreover, statistical significance is not a definitive proof. It's based on probability, so there's always a chance of making an error. Researchers could conclude there's an effect when there isn't one (a false positive, or Type I error), or they could fail to detect an effect that is there (a false negative, or Type II error).

In conclusion, statistical significance is a valuable tool in experimental research, but it should be interpreted with caution. It provides a measure of the likelihood that the results are not due to chance, but it doesn't guarantee the truth of the findings or their practical importance.