How to Find Level of Significance?

The level of significance, which (α) is predetermined by researchers before starting a hypothesis test, can have crucial impact on the outcomes of a study. This is the risk of erroneously rejecting a true and labeled null hypothesis. In many instances, you can encounter different cut-off points for reliability, for example 0.05, 0.01, and 0.10, and researchers may choose the after the other values.

To test the level of significance, you decide before you perform your statistics test what value you want it to be. The decision in this case is usually a combination of three parameters: the nature of the research question, standards that are set within the given field of study and a desire to maintain an optimal balance between Type I and Type II error rates.

General interpretation of the p-value based upon the level of significance of 10% is added below:

• If the p-value is greater than 0.1 (i.e. p > 0.1), it means we don’t have much evidence against the null hypothesis.
• If the p-value falls between 0.05 and 0.1 (i.e. p > 0.05 and p ≤ 0.1), we have a little bit of evidence against the null hypothesis, but it’s not too strong.
• When the p-value is between 0.01 and 0.05 (i.e. p > 0.01 and p ≤ 0.05), we have stronger evidence against the null hypothesis.
• If the p-value is less than or equal to 0.01 (i.e. p ≤ 0.01), we have very strong evidence against the null hypothesis.

Level of significance or Statistical significance is an important terminology used in Statistics. Level of significance is the measurement of the statistical significance. The level of significance explains whether the null hypothesis is accepted or rejected. In this article, we are going to discuss the level of significance in detail.