Statistical significance is the likelihood that a result or relationship is caused by something other than chance. Statistical hypothesis testing is used to determine whether the result of a data set is statistically significant.
 | | Statistical Significance | | Statistical significance is the likelihood that a relationship between two or more variables is caused by something other than chance. Statistical hypothesis testing is used to determine whether the result of a data set is statistically significant. This test provides a p-value, representing the probability that random chance could explain the result. In general, a p-value of 5% or lower is considered to be statistically significant. | | Read More » | | Related to "Statistical Significance" | | | | Bell Curve | | The term bell curve is used to describe a graphical depiction of a normal probability distribution, whose underlying standard deviations from the median create the curved bell shape. | | Read More » | | | P-test | | A P-test is a statistical method that tests the validity of the null hypothesis which states a commonly accepted claim about a population. | | Read More » | | | Null Hypothesis | | A null hypothesis is a type of hypothesis used in statistics that proposes that no statistical significance exists in a set of given observations. | | Read More » | | | Sampling Error | | A sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data and the results found in the sample do not represent the results that would be obtained from the entire population. | | Read More » | | | | | | CONNECT WITH INVESTOPEDIA | | | | | | | |
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