Type I and Type II Errors

The significance level is 5% or 0.05 to provide a good balance between being too strict or lenient about whether the results of research are significant OTHER THAN WHEN GIVEN OTHERWISE. However, 0.05 gives best chance of avoiding these 2 errors

  • Type I – false positive: It is the mistake of accepting alternative hypothesis by mistake when results are stated to be significant when they’re not. Less likely with strict p level (ALTERNATIVE IS ACCEPTED)

Type II – false negative. Mistake of rejecting alternative hypothesis by mistake i.e. results are stated as not significant when they are. Struct p level makes this more likely (NULL IS ACCEPTED)