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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As far as I understand A)
Answer:
a soda can has diameter 2.5 and 4.8 find surface is divides by 1.98
Answer:
The left cylinder has greater than the right cylinder.
Step-by-step explanation:
Volume of a cylinder is the area of a base times the height.
As the radii are identical, the area of each base is πr²
As the left cylinder has greater height, it also has greater volume.
Answer:
$1683.50
Step-by-step explanation:
You are expected to know that a "mill" is one thousandth of a dollar. In this context, it is the amount of tax on one dollar of assessed valuation. So, the tax amount is found by multiplying the valuation by 18.5/1000:
tax = 0.0185 · $91,000 = $1683.50
