Question

Accepting a false null hypotheses is an example of what?. 1.type II error 2. type 1 erorr.3. probability testing. 4.significance testing​

Answer 1

Step-by-step explanation:

In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding or conclusion), while a type II error is the non-rejection of a false null hypothesis (also known as a "false negative" finding or conclusion[1]). Much of statistical theory revolves around the minimization of one or both of these errors, though the complete elimination of either is a statistical impossibility for non-deterministic algorithms

Answer 2

Answer:

(I am continuing from here ok)

Answer:

By selecting a low threshold (cut -off) value and modifying the alpha (p) level, the quality of the hypothesis test can be increasd. The knowledge of Type I errors and Type II errors is widely used in medical science,biometrics and computer science.

Intuitively,type I errors can be thought of as errors of commission,and type II errors as errors of omission.For example,in the context of binary classification, when trying to decide whether an input image X is an image of a dig: an error of commission (type I) is Classifying X as a dog when it isn't,whereas an error of omission (type II) is classifying X as not a dog when it is.