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.
<h3>
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
#SPJ4
Answer:
84ft
Step-by-step explanation:
The formula to find the area of a parallelogram is b x h.
The base is 14ft and the height is 6ft. Multiply these together.
14 x 6 = 84ft
Answer:
The slope intercept form is y = mx + b, where b is the y-intercept. In the equation y = 2x - 1, the y-intercept is -1. So, if you have an equation like y = 4x, there is no "b" term. Therefore, the y-intercept is zero, and the line passes through
128/6 to the nearest whole number = 21
21(6) = 126
128 - 126 = 2oz
