Level of Significance - Definition, Symbol & Steps | …

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Hypothesis level of significance | Légore Uniformes

If your P value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample gives reasonable evidence to support the alternative hypothesis. It does NOT imply a "meaningful" or "important" difference; that is for you to decide when considering the real-world relevance of your result.

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hypothesis level of significance

The relative costs of false positives and false negatives, and thus the best P value to use, will be different for different experiments. If you are screening a bunch of potential sex-ratio-changing treatments and get a false positive, it wouldn't be a big deal; you'd just run a few more tests on that treatment until you were convinced the initial result was a false positive. The cost of a false negative, however, would be that you would miss out on a tremendously valuable discovery. You might therefore set your significance value to 0.10 or more for your initial tests. On the other hand, once your sex-ratio-changing treatment is undergoing final trials before being sold to farmers, a false positive could be very expensive; you'd want to be very confident that it really worked. Otherwise, if you sell the chicken farmers a sex-ratio treatment that turns out to not really work (it was a false positive), they'll sue the pants off of you. Therefore, you might want to set your significance level to 0.01, or even lower, for your final tests.

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The significance level (alpha) is the probability of type I error. The power of a test is one minus the probability of type II error (beta). Power should be maximised when selecting statistical methods. If you want to estimate then you must understand all of the terms mentioned here.

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Hypothesis, Level of Significance and Critical Value..

After you do a statistical test, you are either going to reject or accept the null hypothesis. Rejecting the null hypothesis means that you conclude that the null hypothesis is not true; in our chicken sex example, you would conclude that the true proportion of male chicks, if you gave chocolate to an infinite number of chicken mothers, would be less than 50%.

Significance Level - David Lane

The significance level α is the probability of making the wrong decision when the is true. Alpha levels (sometimes just called “significance levels”) are used in . Usually, these tests are run with an alpha level of .05 (5%), but other levels commonly used are .01 and .10.

Null Hypothesis (1 of 4) - David Lane

Another way your data can fool you is when you don't reject the null hypothesis, even though it's not true. If the true proportion of female chicks is 51%, the null hypothesis of a 50% proportion is not true, but you're unlikely to get a significant difference from the null hypothesis unless you have a huge sample size. Failing to reject the null hypothesis, even though it's not true, is a "false negative" or "Type II error." This is why we never say that our data shows the null hypothesis to be true; all we can say is that we haven't rejected the null hypothesis.

The null hypothesis is an hypothesis about a population parameter

An alpha level is the probability of a type I error, or you when it is true. A related term, , is the opposite; the probability of rejecting the alternate hypothesis when it is true.

This graph shows the to the far right.