Statistical significance refers to the likelihood that a result from data generated by testing or experimentation is attributable to a specific cause. It is often measured by the p-value, with a lower value indicating greater significance. It is a determination that a relationship between two or more variables is caused by something other than chance. Statistical significance is used to provide evidence concerning the plausibility of the null hypothesis that hypothesizes that there's nothing more than random chance at work in the data. Statistical hypothesis testing is used to determine whether the result of a data set is statistically significant.
Statistical significance is used to assess the possibility that an observed relationship could be the result of random chance. When an observation shows a weak relationship between two variables or has a small number of data points, it is said to be statistically insignificant. However, when there are more data points showing a more consistent relationship, that correlation is said to be statistically significant. Researchers use statistical significance to assess the likelihood that two variables might share a causal relationship. A Chi-Square test (or a similar test) is used to test ideas about how a categorical variable is spread out. Categorical variables represent groups or categories, like animals or countries. These variables don’t have a normal distribution because they only have a small number of possible values.
Statistical significance is calculated using by comparing the results of your data test to a p-value, which can tell you the probability of certain outcomes assuming that the null hypothesis is true. If researchers determine that this probability is very low, they can eliminate the null hypothesis.