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Significance Tests / Hypothesis Testing

Support or Reject Null Hypothesis

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That’s How to State the Null Hypothesis!

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.

Click the link the skip to the situation you need to support or reject null hypothesis for:

You’ll be asked to convert a word problem into a hypothesis statement in statistics that will include a null hypothesis and an . Breaking your problem into a few small steps makes these problems much easier to handle.

How to Determine a p-Value When Testing a Null Hypothesis

Use these general guidelines to decide if you should reject or keep the null:

The short answer is, as a scientist, you are required to; It’s part of the scientific process. Science uses a battery of processes to prove or disprove theories, making sure than any new hypothesis has no flaws. Including both a null and an alternate hypothesis is one safeguard to ensure your research isn’t flawed. Not including the null hypothesis in your research is considered very bad practice by the scientific community. If you set out to prove an alternate hypothesis without considering it, you are likely setting yourself up for failure. At a minimum, your experiment will likely not be taken seriously.

Not so long ago, people believed that the world was flat.

Null hypothesis, H0: The world is flat.
Alternate hypothesis: The world is round.
Several scientists, including , set out to disprove the null hypothesis. This eventually led to the rejection of the null and the acceptance of the alternate. Most people accepted it — the ones that didn’t created the !. What would have happened if Copernicus had not disproved the it and merely proved the alternate? No one would have listened to him. In order to change people’s thinking, he first had to prove that their thinking was wrong.

To find thevalue for your test statistic:

Broken down into English, that’s H0 (The null hypothesis): μ (the average) = (is equal to) 8.2

This is very helpful for me, I finally understand how to answer such a question I the exam, but I don’t understand where the 0.500 is from
And why to substract it from the z value ?!please clear this up for me as am
Just learning about hypothesis testing, I’d also appreciate if you’d explain to me more about the z tables , are they like standard tables?! For all hypothesis testing ? Am a lil lost so please help!!:(

Note that if the alternative hypothesis is the less-than alternative, you reject H0 only if the test statistic falls in the left tail of the distribution (below –2). Similarly, if Ha is the greater-than alternative, you reject H0 only if the test statistic falls in the right tail (above 2).

In English again, that’s H1 (The alternate hypothesis): μ (the average) ≠ (is not equal to) 8.2
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  • Support or Reject Null Hypothesis in Easy Steps

    Broken down into (somewhat) English, that’s H1 (The hypothesis): μ (the average) (is greater than) 8.2

  • Null and Alternative Hypothesis | Real Statistics Using Excel

    Broken down again into English, that’s H0 (The null hypothesis): μ (the average) ≤ (is less than or equal to) 8.2

  • Welcome to the Journal of Articles in Support of the Null Hypothesis

    The following figure shows the locations of a test statistic and their corresponding conclusions.

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Hypothesis testing - Handbook of Biological Statistics

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.

Significance Tests / Hypothesis Testing - Jerry Dallal

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."

Significance Tests / Hypothesis Testing

Suppose you are testing a claim that the percentage of all women with varicose veins is 25%, and your sample of 100 women had 20% with varicose veins. Then the sample proportion p=0.20. The standard error for your sample percentage is the square root of p(1-p)/n which equals 0.04 or 4%. You find the test statistic by taking the proportion in the sample with varicose veins, 0.20, subtracting the claimed proportion of all women with varicose veins, 0.25, and then dividing the result by the standard error, 0.04. These calculations give you a test statistic (standard score) of –0.05 divided by 0.04 = –1.25. This tells you that your sample results and the population claim in H0 are 1.25 standard errors apart; in particular, your sample results are 1.25 standard errors below the claim.


Now imagine that you are testing those extracts from 1000 different tropical plants to try to find one that will make hair grow. The reality (which you don't know) is that one of the extracts makes hair grow, and the other 999 don't. You do the 1000 experiments and do the 1000 frequentist statistical tests, and you use the traditional significance level of PPPP values less than 0.05, but almost all of them are false positives.

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