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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Answer:
The x represents the value of the point on the x axis or the horizontal line, and the y represents the vertical line. Now, lets solve for the first point. We can first see that it only moves to the left by two from zero, which is basically -2. So, right now we have (-2,y). We then look for the y in which we see that it is down -6 from zero, so it will be (-2,-6). Time to look for the second point. We should get (2,-3). Now, with these two points, it is time to find the slope intercept form.
Step-by-step explanation:
Answer:
77 is the ninth term.
Step-by-step explanation:
You start out with adding 3 and every time you add a new number you add the last number you added to it and add 2.
Answer:
The graph of G(x) is the graph of F(x) stretched vertically, flipped over the x-axis, and shifted 2 units up
Step-by-step explanation:
I like math.
