Statistical significance refers to the likelihood that a relationship between variables is as a result of something other than chance. Statistical hypothesis testing is applied in the determination of whether the result of a set of data is statistically significant. This test produces a p- value that represents the probability that random chance can explain the result. Generally, a p- value of 5% and below is considered statistically significant.
In other words, an observed event is said to be statistically significant if it is very unlikely that this event took place by random chance. An event is said to be statistically significant when its p- value is below a certain threshold that is referred to as the level of significance. The decision and conclusion of a study are drawn after passing the threshold and attaining statistical significance.
Example,
A study conducted on a cancer drug showed that there was a 150 basis point increment in the overall survival over the control group. The results had a p- value of 0.02. this was significant as it fell below the 0.05 level. This resulted in the drug being approved for purposes of further trials.
A p- value can also be said to be the probability that a certain event will occur that is extreme as or more extreme than the observed event. This probability also assumes that extreme events take place with a similar relative frequency as under normal circumstances. In simple terms, a p- value can be said to be a measurement of how unusual an observed event is. An event is said to be more unusual when the p- value is lower.
Statistical significance is used to either accept or reject the null hypothesis. This hypothesizes that there exists no relationship between the variables that are measured. When a test result is above the p- value, null hypothesis is accepted. In cases where the test result falls below the p- value, the null hypothesis is rejected.
Statistical significance is mainly applied in new pharmaceutical drug trials, testing of vaccines as well as in the study of pathology for purposes of effectiveness testing as well as informing investors on the success of the company at releasing new goods.
NULL HYPOTHESIS
Null hypothesis refers to a type of hypothesis that is used in statistics proposing that there is no statistical significance existing in a set of the observations given.
ONE TAILED TEST
This refers to a statistical test where the critical area of a distribution is either less than or greater than a certain value, but it cannot be both.
ECONOMETRICS
This refers to the application of mathematical and statistical models to economic data for purposes of testing future trends, hypotheses and theories.
