When we reject the null hypothesis and we fail to reject the null hypothesis.
Explain both the approaches, using the rejection region and the p-value.
In a hypothesis test, you're going to look at two propositions: the null hypothesis (or H0 for
short), and the alternative (H1). The alternative hypothesis is what we hope to support. The null
hypothesis, in contrast, is presumed to be true, until the data provide sufficient evidence that it is
not.
The degree of statistical evidence we need in order to “prove” the alternative hypothesis is
the confidence level. The confidence level is simply 1 minus the Type I error rate (alpha, also
referred to as the significance level), which occurs when you incorrectly reject the null
hypothesis. Commonly uses as 95% confidence level, 5% chance of rejecting null value.
Regardless of the alpha level we choose, any hypothesis test has only two possible outcomes:
1. Reject the null hypothesis (p-value <= alpha) and conclude that the alternative
hypothesis is true at the 95% confidence level (or whatever level you've selected).
2. Fail to reject the null hypothesis (p-value > alpha) and conclude that not enough
evidence is available to suggest the null is false at the 95% confidence level.
Interpretation of Hypothesis test:
Fact 1: Confidence level + alpha = 1
Fact 2: If the p-value is low, the null must go.
Fact 3: The confidence interval and p-value will always lead you to the same conclusion.
http://blog.minitab.com/blog/michelle-paret/alphas-p-values-confidence-intervals-oh-my
This study source was downloaded by 100000829957125 from CourseHero.com on 01-19-2022 11:42:36 GMT -06:00
https://www.coursehero.com/file/32837904/MATH-533-Week-7-Discussiondocx/
Explain both the approaches, using the rejection region and the p-value.
In a hypothesis test, you're going to look at two propositions: the null hypothesis (or H0 for
short), and the alternative (H1). The alternative hypothesis is what we hope to support. The null
hypothesis, in contrast, is presumed to be true, until the data provide sufficient evidence that it is
not.
The degree of statistical evidence we need in order to “prove” the alternative hypothesis is
the confidence level. The confidence level is simply 1 minus the Type I error rate (alpha, also
referred to as the significance level), which occurs when you incorrectly reject the null
hypothesis. Commonly uses as 95% confidence level, 5% chance of rejecting null value.
Regardless of the alpha level we choose, any hypothesis test has only two possible outcomes:
1. Reject the null hypothesis (p-value <= alpha) and conclude that the alternative
hypothesis is true at the 95% confidence level (or whatever level you've selected).
2. Fail to reject the null hypothesis (p-value > alpha) and conclude that not enough
evidence is available to suggest the null is false at the 95% confidence level.
Interpretation of Hypothesis test:
Fact 1: Confidence level + alpha = 1
Fact 2: If the p-value is low, the null must go.
Fact 3: The confidence interval and p-value will always lead you to the same conclusion.
http://blog.minitab.com/blog/michelle-paret/alphas-p-values-confidence-intervals-oh-my
This study source was downloaded by 100000829957125 from CourseHero.com on 01-19-2022 11:42:36 GMT -06:00
https://www.coursehero.com/file/32837904/MATH-533-Week-7-Discussiondocx/
