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This chapter shows how a single sample can be used to construct confidence intervals and test hypotheses about population parameters. Hypothesis testing, also known as testing for significance, is a fundamental part of inferential econometrics.

Hypothesis testing can be confusing, but it has a coherent, stable framework that should help you organize the complicated details. The next section demonstrates that there is a sampling distribution for each sample statistic that is a random variable. Section 16.3 will explain how confidence intervals are constructed and interpreted. We then turn to the logic of hypothesis testing (Section 16.4) and explain why the t distribution is so often used (Section 16.5). The chapter’s last section puts the ideas into practice by working on a real-world example.

It is important to distinguish between *biological* null and alternative hypotheses and *statistical* null and alternative hypotheses. "Sexual selection by females has caused male chickens to evolve bigger feet than females" is a biological alternative hypothesis; it says something about biological processes, in this case sexual selection. "Male chickens have a different average foot size than females" is a statistical alternative hypothesis; it says something about the numbers, but nothing about what caused those numbers to be different. The biological null and alternative hypotheses are the first that you should think of, as they describe something interesting about biology; they are two possible answers to the biological question you are interested in ("What affects foot size in chickens?"). The statistical null and alternative hypotheses are statements about the data that should follow from the biological hypotheses: if sexual selection favors bigger feet in male chickens (a biological hypothesis), then the average foot size in male chickens should be larger than the average in females (a statistical hypothesis). If you reject the statistical null hypothesis, you then have to decide whether that's enough evidence that you can reject your biological null hypothesis. For example, if you don't find a significant difference in foot size between male and female chickens, you could conclude "There is no significant evidence that sexual selection has caused male chickens to have bigger feet." If you do find a statistically significant difference in foot size, that might not be enough for you to conclude that sexual selection caused the bigger feet; it might be that males eat more, or that the bigger feet are a developmental byproduct of the roosters' combs, or that males run around more and the exercise makes their feet bigger. When there are multiple biological interpretations of a statistical result, you need to think of additional experiments to test the different possibilities.

Usually, the null hypothesis is boring and the alternative hypothesis is interesting. For example, let's say you feed chocolate to a bunch of chickens, then look at the sex ratio in their offspring. If you get more females than males, it would be a tremendously exciting discovery: it would be a fundamental discovery about the mechanism of sex determination, female chickens are more valuable than male chickens in egg-laying breeds, and you'd be able to publish your result in *Science* or *Nature*. Lots of people have spent a lot of time and money trying to change the sex ratio in chickens, and if you're successful, you'll be rich and famous. But if the chocolate doesn't change the sex ratio, it would be an extremely boring result, and you'd have a hard time getting it published in the *Eastern Delaware Journal of Chickenology*. It's therefore tempting to look for patterns in your data that support the exciting alternative hypothesis. For example, you might look at 48 offspring of chocolate-fed chickens and see 31 females and only 17 males. This looks promising, but before you get all happy and start buying formal wear for the Nobel Prize ceremony, you need to ask "What's the probability of getting a deviation from the null expectation that large, just by chance, if the boring null hypothesis is really true?" Only when that probability is low can you reject the null hypothesis. The goal of statistical hypothesis testing is to estimate the probability of getting your observed results under the null hypothesis.

Recall that we are ultimately always interested in drawing conclusions *about the population*, *not the particular sample we observed*. In the simple regression setting, we are often interested in learning about the population intercept *β*_{} and the population slope *β*_{}. As you know, confidence intervals and hypothesis tests are two related, but different, ways of learning about the values of population parameters. Here, we will learn how to calculate confidence intervals and conduct hypothesis tests for both *β*_{} and *β*_{}.

The relative costs of false positives and false negatives, and thus the best *P* value to use, will be different for different experiments. If you are screening a bunch of potential sex-ratio-changing treatments and get a false positive, it wouldn't be a big deal; you'd just run a few more tests on that treatment until you were convinced the initial result was a false positive. The cost of a false negative, however, would be that you would miss out on a tremendously valuable discovery. You might therefore set your significance value to 0.10 or more for your initial tests. On the other hand, once your sex-ratio-changing treatment is undergoing final trials before being sold to farmers, a false positive could be very expensive; you'd want to be very confident that it really worked. Otherwise, if you sell the chicken farmers a sex-ratio treatment that turns out to not really work (it was a false positive), they'll sue the pants off of you. Therefore, you might want to set your significance level to 0.01, or even lower, for your final tests.

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Recall that, in general, we want our confidence intervals to be as narrow as possible. If we know what factors affect the length of a confidence interval for the slope *β*_{}, we can control them to ensure that we obtain a narrow interval. The factors can be easily determined by studying the formula for the confidence interval:

The resulting confidence interval gives us a range of values that is likely to contain the true unknown value *β*_{}. The factors affecting the length of a confidence interval for *β*_{} are identical to the factors affecting the length of a confidence interval for *β*_{}.** **

You should decide whether to use the one-tailed or two-tailed probability before you collect your data, of course. A one-tailed probability is more powerful, in the sense of having a lower chance of false negatives, but you should only use a one-tailed probability if you really, truly have a firm prediction about which direction of deviation you would consider interesting. In the chicken example, you might be tempted to use a one-tailed probability, because you're only looking for treatments that decrease the proportion of worthless male chickens. But if you accidentally found a treatment that produced 87% male chickens, would you really publish the result as "The treatment did not cause a significant decrease in the proportion of male chickens"? I hope not. You'd realize that this unexpected result, even though it wasn't what you and your farmer friends wanted, would be very interesting to other people; by leading to discoveries about the fundamental biology of sex-determination in chickens, in might even help you produce more female chickens someday. Any time a deviation in either direction would be interesting, you should use the two-tailed probability. In addition, people are skeptical of one-tailed probabilities, especially if a one-tailed probability is significant and a two-tailed probability would not be significant (as in our chocolate-eating chicken example). Unless you provide a very convincing explanation, people may think you decided to use the one-tailed probability *after* you saw that the two-tailed probability wasn't quite significant, which would be cheating. It may be easier to always use two-tailed probabilities. **For this handbook, I will always use two-tailed probabilities, unless I make it very clear that only one direction of deviation from the null hypothesis would be interesting.**

Let's see how we can use Minitab to calculate confidence intervals and conduct hypothesis tests for the slope *β*_{}. Minitab's regression analysis output for our skin cancer mortality and latitude example appears below.

In the olden days, when people looked up *P* values in printed tables, they would report the results of a statistical test as "*P**P**P*>0.10", etc. Nowadays, almost all computer statistics programs give the exact *P* value resulting from a statistical test, such as *P*=0.029, and that's what you should report in your publications. You will conclude that the results are either significant or they're not significant; they either reject the null hypothesis (if *P* is below your pre-determined significance level) or don't reject the null hypothesis (if *P* is above your significance level). But other people will want to know if your results are "strongly" significant (*P* much less than 0.05), which will give them more confidence in your results than if they were "barely" significant (*P*=0.043, for example). In addition, other researchers will need the exact *P* value if they want to combine your results with others into a .

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