Statistical significance is a mathematical tool used to determine whether the outcome of an experiment is the result of a relationship between specific factors or due to chance. Statistical significance is commonly used in the medical field to test drugs and vaccines and to determine causal factors of disease. Statistical significance is also used in the fields of psychology, environmental biology, and any other discipline that conducts research through experimentation.

Statistics are the mathematical calculations of numeric sets or populations that are manipulated to produce a probability of the occurrence of an event. Statistics use a numeric sample and apply that number to an entire population. For the sake of example, we might say that 80% of all Americans drive a car. It would be difficult to question every American about whether or not they drive a car, so a random number of people would be questioned and then the data would be statistically analyzed and generalized to account for everyone.

In a scientific study, a theory is proposed, then data is collected and analyzed. The statistical analysis of the data will produce a number that is statistically significant if it falls below 5%, which is called the confidence level. In other words, if the likelihood of an event is statistically significant, the researcher can be 95% confident that the result did not happen by chance.

Sometimes, when the statistical significance of an experiment is very important, such as the safety of a drug meant for humans, the statistical significance must fall below 3%. In this case, a researcher could be 97% sure that a particular drug is safe for human use. This number can be lowered or raised to accommodate the importance and desired certainty of the result being correct.

Statistical significance is used to reject or accept what is called the null hypothesis. A hypothesis is a statement of the theory that a researcher is trying to prove. The null hypothesis holds that the factors a researcher is looking at have no effect on differences in the data. Statistical significance is usually written, for example, t=.02, p<.05. Here, "t" stands for the statistic test score and "p<.05" means that the probability of an event occurring by chance is less than 5%. These numbers would cause the null hypothesis to be rejected, therefore affirming that the particular theory is true.

Here is an example of a psychological theory using statistical significance: It is theorized that baby girls smile more than baby boys. In order to test this theory, a researcher would observe a certain number of baby girls and boys and count how many times they smile. At the end of the observation, the numbers of smiles would be statistically analyzed.

Every experiment comes with a certain degree of error. It is possible that on the day of observation all the boys were abnormally grumpy. The statistical significance found by the analysis of the data would rule out this possibility by 95% if t=.03. In this case, the null hypothesis that baby girls do not smile more than baby boys would be rejected, and with 95% certainty, the researcher could say that girls smile more than boys.