Alpha Risk


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Alpha Risk

When testing a hypothesis, the risk of rejecting a piece of data that should have been accepted. Many tests reject some data as unusable or irrelevant. Alpha risk is the probability that the wrong data will be eliminated from the sample. It is also called type I error or alpha error. See also: Beta risk.
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Similar to the alpha risk, a large prediction error (MPE), such as that for the X-11 model, can lead to anomalies resulting in very low or high beta risks regardless of the width of the confidence interval.
TABLE 4 Relationships Among Confidence Interval Width, Risk, and Testing Approach (a) Confidence Interval Width Risk Level Alpha Risk ([absolute value of E]=0): Positive Approach Wider Lower (Control Point) Negative Approach Wider Higher Beta Risk ([absolute value of E]=M): Positive Approach Wider Higher Negative Approach Wider Lower (Control Point) (a) Assuming the predicted values (P) and actual values (A) are reasonably close.
As a result, the alpha risk for the positive approach is expected to be the same for all the models because a common rejection area ([alpha] =.33) is applied to each model.
For the positive approach, it is apparent from table 6 that the ARIMA and Martingale models yield the lowest alpha risk. The alpha risk for both of these models is closer to the specified control point ([absolute value of E]=0) risk of [alpha] =.33.
In term of alpha risk, the structural and stepwise models yield the lowest alpha risk for both time periods for the negative approach.
This would cause a large alpha risk and a small beta risk.