State Null and Alternative Hypotheses

True. Just by chance it is possible to get a samplethat produces a small p-value,even though the null hypothesis is true. This is called a Type Ierror. A Type II error is when the null hypothesis is notrejectedwhen it is in fact false.

m) Ifyou get a p-value of 0.13, it means thatthe null hypothesis is true in 13% of all samples.

Hypothesis testing is very important in the scientific community and is necessary for advancing theories and ideas. Statistical hypothesis tests are not just designed to select the more likely of two hypotheses—a test will remain with the null hypothesis until there's enough evidence to support the alternative hypothesis. Now you have seen several examples of hypothesis testing and you can better understand why it is so important. For more information on types of hypotheses see .


State Null and Alternative Hypotheses

This is smaller than our alpha value of .05. That means we should reject the null hypothesis.

Alternatively, a two-tailed prediction means that we do not make a choice over the direction that the effect of the experiment takes. Rather, it simply implies that the effect could be negative or positive. If Sarah had made a two-tailed prediction, the alternative hypothesis might have been:


Explainer: what is a null hypothesis? - The Conversation

Hypotheses developers and testers usually hope that the null hypothesis is rejected and their alternative hypothesis supported – that the drug they’re testing is effective; the campaign they’re running is a success; that the light is bent by gravity as predicted by Newtonian physics and Einstein’s theory of relativity …

Symbol for null hypothesis in word - …

Hypothesis testing can be one of the most confusing aspects for students, mostly because before you can even perform a test, you have to know what your is. Often, those tricky word problems that you are faced with can be difficult to decipher. But it’s easier than you think; all you need to do is:

What is another word for hypothesis

So, you might get a p-value such as 0.03 (i.e., p = .03). This means that there is a 3% chance of finding a difference as large as (or larger than) the one in your study given that the null hypothesis is true. However, you want to know whether this is "statistically significant". Typically, if there was a 5% or less chance (5 times in 100 or less) that the difference in the mean exam performance between the two teaching methods (or whatever statistic you are using) is as different as observed given the null hypothesis is true, you would reject the null hypothesis and accept the alternative hypothesis. Alternately, if the chance was greater than 5% (5 times in 100 or more), you would fail to reject the null hypothesis and would not accept the alternative hypothesis. As such, in this example where p = .03, we would reject the null hypothesis and accept the alternative hypothesis. We reject it because at a significance level of 0.03 (i.e., less than a 5% chance), the result we obtained could happen too frequently for us to be confident that it was the two teaching methods that had an effect on exam performance.

Statisticians will never accept the null hypothesis, ..

I don’t know how to explain to this that he is erroneously using p-values when he claims that “the odds are” (1 – p)/p that a null hypothesis is false. Maybe others want to jump in here?