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Support or Reject Null Hypothesis
When you about a , you can use your test statistic to decide whether to reject the null hypothesis, H_{0}. You make this decision by coming up with a number, called a value.
Note that if the alternative hypothesis is the lessthan alternative, you reject H_{0} only if the test statistic falls in the left tail of the distribution (below –2). Similarly, if H_{a} is the greaterthan alternative, you reject H_{0} only if the test statistic falls in the right tail (above 2).
That’s How to State the Null Hypothesis!
There are several approaches that can be used to test hypotheses concerning two independent proportions. Here we present one approach  the chisquare test of independence is an alternative, equivalent, and perhaps more popular approach to the same analysis. Hypothesis testing with the chisquare test is addressed in the third module in this series: BS704_HypothesisTestingChiSquare.
In the two independent samples application with a continuous outcome, the parameter of interest in the test of hypothesis is the difference in population means, μ_{1}μ_{2}. The null hypothesis is always that there is no difference between groups with respect to means, i.e.,
How to Determine a pValue When Testing a Null Hypothesis
Here we use the proportion specified in the null hypothesis as the true proportion of successes rather than the sample proportion. If we fail to satisfy the condition, then alternative procedures, called exact methods must be used to test the hypothesis about the population proportion.
Hypothesis testing applications with a dichotomous outcome variable in a single population are also performed according to the fivestep procedure. Similar to tests for means, a key component is setting up the null and research hypotheses. The objective is to compare the proportion of successes in a single population to a known proportion (p_{0}). That known proportion is generally derived from another study or report and is sometimes called a historical control. It is important in setting up the hypotheses in a one sample test that the proportion specified in the null hypothesis is a fair and reasonable comparator.
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We reject the null hypothesis because 6.15
We do not reject H_{0} because 0.96 > 2.145. We do not have statistically significant evidence at α=0.05 to show that the mean total cholesterol level is lower than the national mean in patients taking the new drug for 6 weeks. Again, because we failed to reject the null hypothesis we make a weaker concluding statement allowing for the possibility that we may have committed a Type II error (i.e., failed to reject H_{0} when in fact the drug is efficacious).
Null and Alternative Hypothesis  Real Statistics Using …
Recall that when we fail to reject H_{0} in a test of hypothesis that either the null hypothesis is true (here the mean expenditures in 2005 are the same as those in 2002 and equal to $3,302) or we committed a Type II error (i.e., we failed to reject H_{0} when in fact it is false). In summarizing this test, we conclude that we do not have sufficient evidence to reject H_{0}. We do not conclude that H_{0} is true, because there may be a moderate to high probability that we committed a Type II error. It is possible that the sample size is not large enough to detect a difference in mean expenditures.
Support or Reject Null Hypothesis in Easy Steps
In the second experiment, you are going to put human volunteers with high blood pressure on a strict lowsalt diet and see how much their blood pressure goes down. Everyone will be confined to a hospital for a month and fed either a normal diet, or the same foods with half as much salt. For this experiment, you wouldn't be very interested in the P value, as based on prior research in animals and humans, you are already quite certain that reducing salt intake will lower blood pressure; you're pretty sure that the null hypothesis that "Salt intake has no effect on blood pressure" is false. Instead, you are very interested to know how much the blood pressure goes down. Reducing salt intake in half is a big deal, and if it only reduces blood pressure by 1 mm Hg, the tiny gain in life expectancy wouldn't be worth a lifetime of bland food and obsessive labelreading. If it reduces blood pressure by 20 mm with a confidence interval of ±5 mm, it might be worth it. So you should estimate the effect size (the difference in blood pressure between the diets) and the confidence interval on the difference.
How to Determine a pValue When Testing a Null Hypothesis
Hypothesis testing applications with a continuous outcome variable in a single population are performed according to the fivestep procedure outlined above. A key component is setting up the null and research hypotheses. The objective is to compare the mean in a single population to known mean (μ_{0}). The known value is generally derived from another study or report, for example a study in a similar, but not identical, population or a study performed some years ago. The latter is called a historical control. It is important in setting up the hypotheses in a one sample test that the mean specified in the null hypothesis is a fair and reasonable comparator. This will be discussed in the examples that follow.
What does the null and alternative hypothesis mean in …
Here are three experiments to illustrate when the different approaches to statistics are appropriate. In the first experiment, you are testing a plant extract on rabbits to see if it will lower their blood pressure. You already know that the plant extract is a diuretic (makes the rabbits pee more) and you already know that diuretics tend to lower blood pressure, so you think there's a good chance it will work. If it does work, you'll do more lowcost animal tests on it before you do expensive, potentially risky human trials. Your prior expectation is that the null hypothesis (that the plant extract has no effect) has a good chance of being false, and the cost of a false positive is fairly low. So you should do frequentist hypothesis testing, with a significance level of 0.05.
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