The significance level, commonly known as alpha or α, is a measure of the degree of evidence required in your model before rejecting the null hypothesis. Before conducting experiments, the researcher establishes the degree of relevance. The significance level represents the likelihood of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 represents a 5% chance of determining that there is a difference when there is none. Low significance levels suggest that more evidence is needed to reject the null hypothesis. Use significance levels during hypothesis testing to help you decide which hypothesis the data supports. Compare your p-value and significance level. If the p-value falls below your significance level, you can reject the null hypothesis and conclude that the effect is statistically significant. In other words, the evidence in your sample is sufficient to reject the null hypothesis on a population level.
Significance levels are an essential aspect of hypothesis testing in statistics. However, unlike the other variables in your statistical report, statistical software does not calculate the significance level. Instead, you select a degree of significance. In this essay, I will explain conceptually the importance level, why you pick its value, and how to select a decent value. In statistics, the degree of significance is also known as alpha (α).
First and foremost, remember that hypothesis tests are inferential techniques. These tests establish if your sample evidence is strong enough to show a population-wide effect. Assume you're comparing the means of two groups. Your sample data demonstrates that there is a discrepancy between the means. Does the sample variance indicate a difference between the two populations? Is the discrepancy due to a random sampling error? That is where hypothesis testing comes in! Your biological sample data demonstrates an effect. The level of significance indicates how strong the sample evidence must be before determining whether the results are statistically significant.