Reviewed by Oct 05, 2020| Updated on
The beta risk is the likelihood that a statistical test will consider a false null hypothesis. This is also referred to as a type II error or consumer risk. The term "risk" in this context refers to chance or a likelihood of making an incorrect decision.
The principal determinant of beta exposure is the sample size used for the study. The larger the sample tested, the lower the beta-risk becomes. Beta risk, when an alternative hypothesis is correct, can be defined as the risk found in incorrectly accepting the null hypothesis.
Simply put, it takes the view that there is no difference when there is, in fact, one. Beta risk is often referred to as "beta error," which is sometimes paired with "alpha risk," commonly referred to as a type I error.
Alpha risk is an error that occurs when a null hypothesis is turned down when it is true. The best way to minimise alpha risk is to increase the size of the sample being tested, in the expectation that the tremendous example would be more representative of the population.
Beta risk is based on the nature and character of a decision being taken and may be determined by a company or individual. It depends on the magnitude of the sample mean-variance. The way beta risk is managed is by boosting the sample size of the test. In decision making, a reasonable amount of beta risk is around 10 per cent. Any number higher will cause sample size increases.
The Altman Z-score can be used to make a new application of the hypothesis testing in finance. The Z-score is a mathematical model designed to forecast a company's potential bankruptcy based on specific financial indicators.
Statistical studies of Z-score accuracy have demonstrated relatively high precision, predicting bankruptcy within one year. These tests show a beta risk ranging from about 15 per cent to 20 per cent, depending on the sample being tested (firms predicted to go bankrupt but did not).