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Alpha Risk Definition
It can be defined as the risk of incorrectly deciding to reject the null hypothesis. If the confidence interval is 95% then the alpha risk is 5% or 0.05 and for example, there is a 5% chance that a part has been determined defective when it is not. One has observed or made a decision that a difference exists but there really is none.
So the Alpha error is also called False Positive as well as Type I Error and the Confidence Level = 1 - Alpha
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It is called the significance level of a test and the level of significance is commonly between 1% or 10% but can be any value. It depends on the desired level of confidence as well as the need to reduce Type I error. Selecting 5% signifies that there is a chance of 5 % that the observed variation is not actually the truth and so the amount of risk that is willing to accept of making a Type I error.
Since the alpha risk is the risk in a statistical test that a null hypothesis will be rejected when it is actually true so it is also known as a Type I error. The best & better way to reduce alpha risk is to increase the size of the sample being tested with the larger sample being more representative of the population.
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An example of alpha risk in finance would be if one wanted to test the hypothesis that the average yearly return on a group of equities was greater than 10%, so the null hypothesis would be if the returns were equal to or less than 10%. n order to test this, one would compile a sample of equity returns over time and set the level of significance by statistically looking at the sample.
It is required to determine that the average yearly return is higher than 10%, you would reject the null hypothesis, but the average return was 6% so a type I error is made. The probability that has made this error in the test is the alpha risk and this alpha risk could lead to invest in a group of equities when the returns do not actually justify the potential risks.
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