Type I error says that we suppose that the null hypothesis exists rejected when in reality the null hypothesis was actually true.
Type II error says that we suppose that the null hypothesis exists taken when in fact the null hypothesis stood actually false.
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What is
Type I error and Type II error?</h3>
In statistics, a Type I error exists as a false positive conclusion, while a Type II error exists as a false negative conclusion.
Making a statistical conclusion still applies uncertainties, so the risks of creating these errors exist unavoidable in hypothesis testing.
The probability of creating a Type I error exists at the significance level, or alpha (α), while the probability of making a Type II error exists at beta (β). These risks can be minimized through careful planning in your analysis design.
Examples of Type I and Type II error
- Type I error (false positive): the testing effect says you have coronavirus, but you actually don’t.
- Type II error (false negative): the test outcome says you don’t have coronavirus, but you actually do.
To learn more about Type I and Type II error refer to:
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Im pretty sure it was d when i took the test hopefully im corecct sorry if im not
No, the work is correct, but the interpretation is not. “6 is less than or equal to h<span>” means that h is greater than or equal to 6. He must grow at least 6 inches, not at most 6 inches.</span>
The answer is 52.94 because since it said he made to deposits of 89.32 you and that together and your result is 173.64 and then you subtract that from -125.70 and you get 52.94
Find the median and mean of the data set below: 6 , 37 , 2 , 38 , 1 , 3 , 46 6,37,2,38,1,3,46
IgorLugansk [536]
Answer:
Mean: 19
Median: 6
Step-by-step explanation:
