Final answer:
Statistical significance means that the probability of the test result occurring by chance is low, usually below a threshold such as a p-value of 0.05. This implies confidence in the test result and often leads to the rejection of the null hypothesis.
Step-by-step explanation:
When we say the result of a test has statistical significance, we mean that the test result is unlikely to have occurred by chance. More specifically, statistical significance is a determination that the observed effect in data is unlikely to be happening due to random variation, and is instead most likely due to some specific cause or effect under investigation. Typically, a p-value is used to measure the probability that the observed results are produced by random chance—the smaller the p-value, the lower the probability that the results are due to chance. In practical terms, a p-value below a certain threshold (often 0.05 or 5%) indicates statistical significance. For instance, if an experiment yields a p-value of 0.03, it suggests that there is only a 3% probability that the observed difference happened by chance and is considered statistically significant.
By using a statistical hypothesis test like a t-test, researchers compare the mean values of data against each other or an expected value to determine whether there are significant differences that support the alternative hypothesis. If an outcome has statistical significance, it implies that we can be fairly confident in the reliability of the results and consider the null hypothesis as potentially false.