Stats what is alpha

Monday a. Tuesday a. Wednesday a. Thursday a. Friday a. Saturday a. If there is not a coach on duty, submit your question via one of the below methods:. Ask a Coach. In hypothesis testing, there are two important values you should be familiar with: alpha and beta. These values are used to determine how meaningful the results of the test are.

Alpha is also known as the level of significance. This represents the probability of obtaining your results due to chance. Our sample mean We can also see if it is statistically significant using the other common significance level of 0. The two shaded areas each have a probability of 0. This time our sample mean does not fall within the critical region and we fail to reject the null hypothesis.

This comparison shows why you need to choose your significance level before you begin your study. It protects you from choosing a significance level because it conveniently gives you significant results! Thanks to the graph, we were able to determine that our results are statistically significant at the 0. P-values are the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis.

This definition of P values, while technically correct, is a bit convoluted. To graph the P value for our example data set, we need to determine the distance between the sample mean and the null hypothesis value In the graph above, the two shaded areas each have a probability of 0.

This probability represents the likelihood of obtaining a sample mean that is at least as extreme as our sample mean in both tails of the distribution if the population mean is When a P value is less than or equal to the significance level, you reject the null hypothesis.

If we take the P value for our example and compare it to the common significance levels, it matches the previous graphical results. The P value of 0. If we stick to a significance level of 0. A common mistake is to interpret the P-value as the probability that the null hypothesis is true.

To determine if an observed outcome is statistically significant, we compare the values of alpha and the p-value. There are two possibilities that emerge:.

The implication of the above is that the smaller the value of alpha is, the more difficult it is to claim that a result is statistically significant. On the other hand, the larger the value of alpha is the easier is it to claim that a result is statistically significant.

Coupled with this, however, is the higher probability that what we observed can be attributed to chance. Actively scan device characteristics for identification.

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