Statistical significance is a mathematical tool that is used to determine whether the outcome of an experiment is the result of a relationship between specific factors or merely the result of chance. This concept is commonly used in the medical field to test drugs and vaccines and to determine causal factors of disease. Statistical significance also is used in the fields of psychology, environmental biology and other disciplines in which research is conducted 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. A sample is used, and the results of the calculation are applied to an entire population. For example, it might be said that 80 percent of all adults in the United States drive a car. It would be difficult to ask each adult in the U.S. whether whether he or she drives a car, so a random number of people could be questioned, and the data could be statistically analyzed and generalized to apply to all adults in the U.S.

In a scientific study, a hypothesis 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 a certain percentage called the confidence level or level of significance. For example, if this level is set at 5 percent and the likelihood of an event is determined to be statistically significant, the researcher is 95 percent 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 percent. In this case, a researcher could be 97 percent 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 an explanation that a researcher is trying to prove. The null hypothesis typically holds that the factors at which a researcher is looking have no effect on differences in the data or that there is no connection between the factors. Statistical significance is usually written, for example, as *t=.02, p<.05*. Here, "t" stands for the test score and "p<.05" means that the probability of an event occurring by chance is less than 5 percent. These numbers would cause the null hypothesis to be rejected.

An example of a psychological hypothesis using statistical significance might be the hypothesis that baby girls smile more than baby boys. To test this hypothesis, a researcher would observe a certain number of baby girls and boys and count how many times they smile within a certain period of time. 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 percent if t=.03. In this case, with 95 percent certainty, the researcher could say that girls smile more than boys.