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d. a null hypothesis for evolution

Let's return finally to the question of whether we reject or fail to reject the null hypothesis.

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b. Conclusions must support the null hypothesis

So, you might get a p-value such as 0.03 (i.e., p = .03). This means that there is a 3% chance of finding a difference as large as (or larger than) the one in your study given that the null hypothesis is true. However, you want to know whether this is "statistically significant". Typically, if there was a 5% or less chance (5 times in 100 or less) that the difference in the mean exam performance between the two teaching methods (or whatever statistic you are using) is as different as observed given the null hypothesis is true, you would reject the null hypothesis and accept the alternative hypothesis. Alternately, if the chance was greater than 5% (5 times in 100 or more), you would fail to reject the null hypothesis and would not accept the alternative hypothesis. As such, in this example where p = .03, we would reject the null hypothesis and accept the alternative hypothesis. We reject it because at a significance level of 0.03 (i.e., less than a 5% chance), the result we obtained could happen too frequently for us to be confident that it was the two teaching methods that had an effect on exam performance.

T F The p-value is the probability that the null hypothesis is true.

Interestingly, there is considerable debate, even among statisticians, regarding the appropriate use of one- versus two-tailed -tests. Some argue that because in reality no two population means are ever identical, that all tests should be one tailed, as one mean must in fact be larger (or smaller) than the other (). Put another way, the null hypothesis of a two-tailed test is always a false premise. Others encourage standard use of the two-tailed test largely on the basis of its being more conservative. Namely, the -value will always be higher, and therefore fewer false-positive results will be reported. In addition, two-tailed tests impose no preconceived bias as to the direction of the change, which in some cases could be arbitrary or based on a misconception. A universally held rule is that one should never make the choice of a one-tailed -test after determining which direction is suggested by your data In other words, if you are hoping to see a difference and your two-tailed -value is 0.06, don't then decide that you really intended to do a one-tailed test to reduce the -value to 0.03. Alternatively, if you were hoping for no significant difference, choosing the one-tailed test that happens to give you the highest -value is an equally unacceptable practice.

An is computed for each hypothesis you are testing.

T F In science we rarely prove the null hypothesis either true or false.

The Central Limit Theorem having come to our rescue, we can now set aside the caveat that the populations shown in are non-normal and proceed with our analysis. From we can see that the center of the theoretical distribution (black line) is 11.29, which is the actual difference we observed in our experiment. Furthermore, we can see that on either side of this center point, there is a decreasing likelihood that substantially higher or lower values will be observed. The vertical blue lines show the positions of one and two SDs from the apex of the curve, which in this case could also be referred to as SEDMs. As with other SDs, roughly 95% of the area under the curve is contained within two SDs. This means that in 95 out of 100 experiments, we would expect to obtain differences of means that were between “8.5” and “14.0” fluorescence units. In fact, this statement amounts to a 95% CI for the difference between the means, which is a useful measure and amenable to straightforward interpretation. Moreover, because the 95% CI of the difference in means does not include zero, this implies that the -value for the difference must be less than 0.05 (i.e., that the null hypothesis of no difference in means is not true). Conversely, had the 95% CI included zero, then we would already know that the -value will not support conclusions of a difference based on the conventional cutoff (assuming application of the two-tailed -test; see below).

How do you know which hypothesis to put in H0 and which one to put in Ha? Typically, the null hypothesis says that nothing new is happening; the previous result is the same now as it was before, or the groups have the same average (their difference is equal to zero). In general, you assume that people’s claims are true until proven otherwise. So the question becomes: Can you prove otherwise? In other words, can you show sufficient evidence to reject H0?

How to Set Up a Hypothesis Test: Null versus Alternative

In a research which one is the one being tested : the null hypothesis or the hypothesis?

Looking at , we can see that the answer is just the proportion of the area under the curve that lies to the of positive 11.3 (solid vertical blue line). Because the graph is perfectly symmetrical, the -value for this right-tailed test will be exactly half the value that we determined for the two-tailed test, or 0.013. Thus in cases where the direction of the difference coincides with a directional research hypothesis, the -value of the one-tailed test will always be half that of the two-tailed test. This is a useful piece of information. Anytime you see a -value from a one-tailed -test and want to know what the two-tailed value would be, simply multiply by two.

Most importantly, the -value for this test will answer the question: If the null hypothesis is true, what is the probability that the following result could have occurred by chance sampling?

All null hypotheses include an equal sign in them.
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Hypothesis dictionary definition | hypothesis defined

The good news is that, whenever possible, we will take advantage of the test statistics and P-values reported in statistical software, such as Minitab, to conduct our hypothesis tests in this course.

Define Null and Alternative Hypotheses

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Before you become distressed about what the title of this section actually means, let's be clear about something. Statistics, in its broadest sense, effectively does two things for us—more or less simultaneously. (1) Statistics provides us with useful quantitative descriptors for summarizing our data. This includes fairly simple stuff such as means and proportions. It also includes more complex statistics such as the correlation between related measurements, the slope of a linear regression, and the odds ratio for mortality under differing conditions. These can all be useful for interpreting our data, making informed conclusions, and constructing hypotheses for future studies. However, statistics gives us something else, too. (2) Statistics also informs us about the accuracy of the very estimates that we've made. What a deal! Not only can we obtain predictions for the population mean and other parameters, we also estimate how accurate those predictions really are. How this comes about is part of the “magic” of statistics, which as stated shouldn't be taken literally, even if it appears to be that way at times.

Analysis of Variance 3 -Hypothesis Test with F-Statistic

For multiple observations in cells, you would also be testing a third hypothesis:
H03: The factors are independent or the interaction effect does not exist.

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Helping my daughter with science fair project. We are using spirometry data from my clinic to see which gender smoking ages the lungs the most. My daughter thinks smoking ages a woman’s lungs the most. However, wouldn’t the null hypothesis be there is no gender difference in lung age?

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