## 09/01/2018 · Evaluating a Hypothesis

### evaluate a hypothesis that has been learned by your algorithm

There are different ways of doing statistics. The technique used by the vast majority of biologists, and the technique that most of this handbook describes, is sometimes called "frequentist" or "classical" statistics. It involves testing a null hypothesis by comparing the data you observe in your experiment with the predictions of a null hypothesis. You estimate what the probability would be of obtaining the observed results, or something more extreme, if the null hypothesis were true. If this estimated probability (the *P* value) is small enough (below the significance value), then you conclude that it is unlikely that the null hypothesis is true; you reject the null hypothesis and accept an alternative hypothesis.

### So how do you tell if the hypothesis might be overfitting

How would you operationally define a variable such as ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others. In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

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.

## A core of scientific methods is hypothesis testing

If your p-value is less than your significance level,you have shown that your sample resultswere unlikely to arise by chance if H_{0} is true. The data are**statistically significant**.You therefore **reject H _{0} and accept H_{1}.**

## Have your say about what you just read!

The p-value is not any of the above because they are all plainprobabilities. Once again,**the p-value is just a measure of how likely your results would be if H_{0} is true and random chance is the only factor in selecting the sample.**

## What Are Examples of a Hypothesis? - ThoughtCo

A Type I error would be condemning an innocent man; aType II error would be letting a guilty man go free. In our legalsystem, a defendant is not supposed to be found guilty if there is areasonable doubt; this would correspond to your α. Probablyα = 0.05 is not good enough in a serious case likemurder, where a Type I error would mean long jail time orexecution, so if you’re on a jury you’d want to be moresure than that.

## let's restate the hypothesis to make it easy to evaluate ..

In the third experiment, you are going to put magnetic hats on guinea pigs and see if their blood pressure goes down (relative to guinea pigs wearing the kind of non-magnetic hats that guinea pigs usually wear). This is a really goofy experiment, and you know that it is very unlikely that the magnets will have any effect (it's not impossible—magnets affect the sense of direction of homing pigeons, and maybe guinea pigs have something similar in their brains and maybe it will somehow affect their blood pressure—it just seems really unlikely). You might analyze your results using Bayesian statistics, which will require specifying in numerical terms just how unlikely you think it is that the magnetic hats will work. Or you might use frequentist statistics, but require a *P* value much, much lower than 0.05 to convince yourself that the effect is real.

## Hypothesis Definition, Checklist, and Examples

They are experts in pharmacology, but are not experts in doing statistical studies, so you will explain to them how statistical studies are done when testing two samples for the effectiveness of a new drug.