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Type I error: The null hypothesis is rejected when it is true.

He then discusses the study of kinship terms, (folk) taxonomies, color terms, prototypes, and taboo and euphrmisms used in different cultures to furthur support the hypothesis.

and let's see how they correspond to the two types of errors in hypothesis testing:

One such idea is the aquatic ape hypothesis (AAH) which attempts to explain a number of human adaptations with the single explanation of a semi-aquatic ancestor.

Type II error: The null hypothesis is not rejected when it is false.

Example of a complex multiple independent variable hypothesis:

You must choose your significance level before you collect the data, of course. If you choose to use a different significance level than the conventional 0.05, people will be skeptical; you must be able to justify your choice. Throughout this handbook, I will always use P If you are doing an experiment where the cost of a false positive is a lot greater or smaller than the cost of a false negative, or an experiment where you think it is unlikely that the alternative hypothesis will be true, you should consider using a different significance level.

One of the major principles of the arousal-performance relationship is the Inverted-U hypothesis (Baechle and Earle 2008, 168), developed by Yerkes and Dodson.

Example of a complex multiple dependent variable hypothesis:

How to Set Up a Hypothesis Test: Null versus Alternative

The connection between the related concepts of logical possibility, causal possibility and conceivability are among the basics which create the foundation for determining the creation and interpretation of a hypothesis that is being analyzed within a thought experiment.

On the contrary, some believe that there is no reason for such hypothesis because some late learners have been able to attain a native-like fluency....

All null hypotheses include an equal sign in them.
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How to Test a Hypothesis for One Population Mean

Lead the reader to your statement of purpose/hypothesis by focusing your literature review from the more general context (the big picture e.g., hormonal modulation of behaviors) to the more specific topic of interest to you (e.g., role/effects of reproductive hormones, especially estrogen, in modulating specific sexual behaviors of mice.)

The null hypothesis is that the population mean,

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.

The notation for the null hypothesis is

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 low-cost 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.

So the null hypothesis in this example is

A Bayesian would insist that you put in numbers just how likely you think the null hypothesis and various values of the alternative hypothesis are, before you do the experiment, and I'm not sure how that is supposed to work in practice for most experimental biology. But the general concept is a valuable one: as Carl Sagan summarized it, "Extraordinary claims require extraordinary evidence."

The three possibilities for the alternative hypothesis, Ha, are

Now instead of testing 1000 plant extracts, imagine that you are testing just one. If you are testing it to see if it kills beetle larvae, you know (based on everything you know about plant and beetle biology) there's a pretty good chance it will work, so you can be pretty sure that a P value less than 0.05 is a true positive. But if you are testing that one plant extract to see if it grows hair, which you know is very unlikely (based on everything you know about plants and hair), a P value less than 0.05 is almost certainly a false positive. In other words, if you expect that the null hypothesis is probably true, a statistically significant result is probably a false positive. This is sad; the most exciting, amazing, unexpected results in your experiments are probably just your data trying to make you jump to ridiculous conclusions. You should require a much lower P value to reject a null hypothesis that you think is probably true.

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