A result of an experiment is said to have statistical significance, or be statistically significant, if it is likely not caused by chance for a given statistical significance level. Your statistical significance level reflects your risk tolerance and confidence level.
A study result is statistically significant if the p-value of the data analysis is less than the prespecified alpha (significance level). In our example, the p-value is 0.02, which is less than the pre-specified alpha of 0.05, so the researcher concludes there is statistical significance for the study.
The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. ... A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.
Statistical significance means that the result observed in a sample is unusual when the null hypothesis is assumed to be true. When testing a hypothesis using the P-value Approach, if the P-value is large, reject the null hypothesis.
Statistical significance is a 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.
What does it mean when a test is statistically significant? it has reached alpha level status. the test statistic had a P-value higher than the alpha level. the test statistic had a P-value lower than the alpha level. it is important in practical terms.
This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).