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:
brainly.com/question/17111420
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Answer:Yes it will remain the same
Step-by-step explanation:
because you are adding the same amount to both sides!
Answer:
C, 200 miles in 4 hours
Step-by-step explanation:
You divide 200 by 4, then you get 50. 50 miles in 1 hour!
Answer:
(4,4)
Step-by-step explanation:
1. I drew a grid and plotted the 3 points I already knew
2. I found the spot that had the same x coordinate that the point to the bottom left of the shape, and the same y coordinate of the top right of the shape which was the point (4,4)
I hope this helps!!
