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:
probably t=40-3h cause of simple math if you don't understand I suggest using khan academy
The equation in y = mx+b form is y = -450x+2745
where,
x = number of hours that have passed by
y = distance to destination in miles
The y intercept is 2745 since this is the initial starting y value, aka the starting distance. Therefore b = 2745.
The slope is -450 or -450/1 since for each hour, the distance goes down by 450. You can think of it as
slope = (change in y)/(change in x)
slope = (change in distance)/(change in time)
slope = (-450 miles)/(1 hour)
slope = -450
