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.
Significance Levels
In biostatistics, the significance level determines the strength of evidence based on likelihood. Alpha, in particular, represents the likelihood that trials will provide statistically significant findings when the null hypothesis is true. Rejecting the genuine null hypothesis is a Type I error. Furthermore, the significance level is equivalent to the type I error rate. You might think of this error rate as the likelihood of a false positive. Test findings may lead you to assume that an impact exists when it does not. When the null hypothesis is valid, we desire a low likelihood of hypothesis tests yielding statistically significant findings. For example, if alpha is 0.05, your analysis has a 5% probability of delivering a significant result when the null hypothesis is true. Just as the standard of proof varies depending on the sort of study, you may adjust the significance threshold for a hypothesis test based on the repercussions of a false positive. Changing alpha allows you to enhance or reduce the amount of evidence required in the sample to establish that the impact occurs in the population.
Changing Levels of Significance
Since 0.05 is the standard alpha, let us begin by moving away from that value. In general, you'll need a compelling cause to modify the significance threshold to anything other than 0.05. Also, take note of the inverse relationship between alpha and the amount of proof necessary. For example, raising the significance level from 0.05 to 0.10 lowers the quality of the evidence. Reducing from 0.05 to 0.01 increases the standard.
Increasing the Level of Significance
Imagine you are testing the quality of a particular drug. You will use the test results to decide which company's drug to buy. Here is a false positive that leads you to buy drugs. The disadvantages of false positives are minimal. As a result, setting the significance level to 0.10 reduces the quantity of evidence required. This adjustment decreases the quantity of evidence necessary, making your test more sensitive in finding changes, but it also raises the likelihood of a false positive by 5%-10%.
Decreasing the Level of Significance
Instead, imagine that you are testing which company's drug lowers blood sugar. A false positive here is very dangerous because organisms are on the line here. You need to be sure that one manufacturer's drug is superior to another manufacturer's drug. In this scenario, adjusting alpha to 0.01 would increase the quantity of proof necessary. This update raises the amount of evidence necessary, making your test less sensitive for finding changes, but it lowers the likelihood of a false positive from 5% to 1%.
Finally, the significance level of 0.05 is increasingly typical. However, it is up to the researcher to establish how much evidence is required to infer that an impact occurs. How severe is a false positive? There is no single correct solution for every case. As a consequence, you must determine the level of importance!
The significance level shows the quantity of evidence required, whereas the p-value reflects the strength of the evidence in your sample. When your p-value is less than or equal to the significance level, the sample evidence meets or surpasses the criteria you set for rejecting the null hypothesis and determining that an effect exists. While this post discusses significance levels conceptually, you may learn about them and p-values through a graphical illustration of how hypothesis testing functions.
Biostatistics Significance: Graphical Illustration Video Tutorial
