In sum, the ecological approach fails due to a Type II error
(accepting the null hypothesis when it is false), which means believing that the available data for decisionmaking are too uncertain from a Knightian perspective to commit substantial resources or commit to irrevocable courses of action in seeking to mitigate climate change.
Type II error
is when we have failed to reject the null hypothesis when, in fact, it is false.
Low statistical power contributes to an increased likelihood of making a Type II error
(Onwuegbuzie, 2004), thereby causing important findings to be either misreported or not even published.
Incorrectly rejecting the null hypothesis is called Type I error and incorrectly accepting the null hypothesis is called Type II error
. For every statistical test, the probability of type I error is called [alpha] and the probability of type II error
is called [beta] as it is shown in table 1.
Power of the study (1-[beta]) ([beta] is type II error
) (Usually 1-[beta] is fixed at > 80%)
Type I and Type II error
concerns in fMRI research: re-balancing the scale.
In the first place, even if any single Type I error is always more costly than a single Type II error
, what matters is the total social cost of all errors, not the cost of any individual error or the relative costs of any two.
A Type II error
takes place when the regression coefficient for level or slope change has a p value > .05 in data series with intervention effect.
Failing to reject [H.sub.o], under the constraints of committing a Type I or Type II error
, is a better decision than simply accepting it, even though the two choices appear to give a similar conclusion.
Type I and Type II errors
. The proportion of cases in which visual analysts detect a nonexistent effect (i.e., commit a Type I error) or miss an existing effect (i.e., a Type II error
) can be used as an indicator of their performance.
It is apparent that the misclassification costs associated with Type I error (a customer with good credit is misclassified as a customer with bad credit) and Type II error
(a customer with bad credit is misclassified as a customer with good credit) are significantly different.
Accept [H.sub.0] when the sequence is generated by a generator that is non random (Type II error