## Null and Alternative Hypothesis | Real Statistics Using …

This criticism only applies to two-tailed tests, where the null hypothesis is "Things are exactly the same" and the alternative is "Things are different." Presumably these critics think it would be okay to do a one-tailed test with a null hypothesis like "Foot length of male chickens is the same as, or less than, that of females," because the null hypothesis that male chickens have smaller feet than females could be true. So if you're worried about this issue, you could think of a two-tailed test, where the null hypothesis is that things are the same, as shorthand for doing two one-tailed tests. A significant rejection of the null hypothesis in a two-tailed test would then be the equivalent of rejecting one of the two one-tailed null hypotheses.

In the second experiment, you are going to put human volunteers with high blood pressure on a strict low-salt 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 label-reading. 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.

## 30/01/2013 · Name: Eston • Tuesday, September 16, 2014

Getting back to -values, let's imagine that in an experiment with mutants, 40% of cross-progeny are observed to be males, whereas 60% are hermaphrodites. A statistical significance test then informs us that for this experiment, = 0.25. We interpret this to mean that even if there was no actual difference between the mutant and wild type with respect to their sex ratios, we would still expect to see deviations as great, or greater than, a 6:4 ratio in 25% of our experiments. Put another way, if we were to replicate this experiment 100 times, random chance would lead to ratios at least as extreme as 6:4 in 25 of those experiments. Of course, you may well wonder how it is possible to extrapolate from one experiment to make conclusions about what (approximately) the next 99 experiments will look like. (Short answer: There is well-established statistical theory behind this extrapolation that is similar in nature to our discussion on the SEM.) In any case, a large -value, such as 0.25, is a red flag and leaves us unconvinced of a difference. It is, however, possible that a true difference exists but that our experiment failed to detect it (because of a small sample size, for instance). In contrast, suppose we found a sex ratio of 6:4, but with a corresponding -value of 0.001 (this experiment likely had a much larger sample size than did the first). In this case, the likelihood that pure chance has conspired to produce a deviation from the 1:1 ratio as great or greater than 6:4 is very small, 1 in 1,000 to be exact. Because this is very unlikely, we would conclude that the null hypothesis is not supported and that mutants really do differ in their sex ratio from wild type. Such a finding would therefore be described as statistically significant on the basis of the associated low -value.

## SSRIs: Much More Than You Wanted To Know | Slate …

Regardless of the method used, the -value derived from a test for differences between proportions will answer the following question: What is the probability that the two experimental samples were derived from the same population? Put another way, the null hypothesis would state that both samples are derived from a single population and that any differences between the sample proportions are due to chance sampling. Much like statistical tests for differences between means, proportions tests can be one- or two-tailed, depending on the nature of the question. For the purpose of most experiments in basic research, however, two-tailed tests are more conservative and tend to be the norm. In addition, analogous to tests with means, one can compare an experimentally derived proportion against a historically accepted standard, although this is rarely done in our field and comes with the possible caveats discussed in . Finally, some software programs will report a 95% CI for the difference between two proportions. In cases where no statistically significant difference is present, the 95% CI for the difference will always include zero.