Rejecting a true null hypothesis.
type1 error is more dangerous
It depends on whether it is the Type I Error or the Type II Error that is increased.
syntax error
error of omission and error of original entry
total composite error double flank
In statistics, there are two types of errors for hypothesis tests: Type 1 error and Type 2 error. Type 1 error is when the null hypothesis is rejected, but actually true. It is often called alpha. An example of Type 1 error would be a "false positive" for a disease. Type 2 error is when the null hypothesis is not rejected, but actually false. It is often called beta. An example of Type 2 error would be a "false negative" for a disease. Type 1 error and Type 2 error have an inverse relationship. The larger the Type 1 error is, the smaller the Type 2 error is. The smaller the Type 2 error is, the larger the Type 2 error is. Type 1 error and Type 2 error both can be reduced if the sample size is increased.
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type1 error is more dangerous
an NMI error
syntax error, Runtime error, Longic error
It depends on whether it is the Type I Error or the Type II Error that is increased.
It depends on whether it is the Type I Error or the Type II Error that is increased.
A logic error.
In hypothesis testing, a Type I error occurs when a true null hypothesis is incorrectly rejected, while a Type II error occurs when a false null hypothesis is not rejected.
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syntax error
Analysis