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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In this task, the error scores are calculated by combining type 1 (when the participant completes the sentence with a word somewhat related to the target word) and type 3 errors (when the individual produces a word that fits the sentence when instructed to produce a word irrelevant to the sentence context) following the classical procedure of giving 1 point to type 1 errors and 3 points to type 3 errors (Burgess & Shallice, 1996).
We also expected that mild and moderate participants would differ more on type 3 errors (reflecting total failure to inhibit the target word) than on type 1 errors (reflecting partial inhibition deficits).
Considering the number of type 1 errors in the inhibition condition, the one-way ANOVA with Group as a between factor showed significant differences for Group (F(2,43) = 13.42, p < 0.01, [[eta].sup.2]= 0.38).
Type 1 errors are a false positive: a researcher states that a specific relationship exists when in fact it does not.
In this case, an overestimation of a given climate impact is analogous to type 1 errors (i.e., a false positive in the magnitude of an impact), while an underestimation of the impact corresponds to type 2 errors (Schneider 2006; Brysse et al.
2013), effectively favoring the risk of type 2 errors to lower the chances of type 1 errors. Yet decision makers often take both type 1 and type 2 errors seriously.
"Right now, credit unions may be making too many Type 1 errors in their loan programs," Rick said.
In lending, correspondingly, a Type 1 error would be rejecting a good loan, while a Type 2 error would be writing a bad loan, the economist said.
Here, only type 1 errors are usually applied, but type 2 errors should also be taken into consideration.
For both design lengths this means that nominal and empirical Type 1 errors did not match for the aforementioned intervention points and there was ah increased probability of false alarm rates or excessive conservativeness for positive and negative serial dependence, respectively.
This result should be interpreted cautiously as the type 1 error in this study is huge.
Now it must be clear that the more the comparisons, more is the chance of type 1 error. So it is recommended that this type of "data torturing" should be avoided.