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An a level of 0.05 means what? what about an a level of 0.01, 0.001, 0.10...

An a level of 0.05 means what? what about an a level of 0.01, 0.001, 0.10 or 0.20?

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Answer #1
  • Alpha levels (sometimes just called “significance levels”) are used in hypothesis tests. Usually, these tests are run with an alpha level of .05 (5%), but other levels commonly used are .01 and .10.
  • The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.
  • The common level of significance and the corresponding confidence level are given below:
  • The level of significance 0.10 is related to the 90% confidence level.
  • The level of significance 0.05 is related to the 95% confidence level.
  • The level of significance 0.01 is related to the 99% confidence level.
  • The actual meaning of a p-value of 0.001 is that there is a one in a thousand probability of finding the result that you did, ‘just by chance alone’ i.e. the result that you found is very rare to have occurred just by chance only.
  • In hypothesis testing, when your p-value is less than the alpha level you selected (typically 0.05), you'd reject the null hypothesis in favor of the alternative hypothesis.
  • Let's say we do a 2-sample t-test to assess the difference between the mean strength of steel from two mills. The null hypothesis says the two means are equal; the alternative hypothesis states that they are not equal.
  • If we get a p-value of 0.02 and we're using 0.05 as our alpha level, we would reject the hypothesis that the population means are equal.
  • If the significance level was set at 0.20,this means that the probability of making a Type I error is 80%, assuming that the null hypothesis is true.
  • Type I error is an error that takes place when the outcome is a rejection of null hypothesis which is, in fact, true. Type II error occurs when the sample results in the acceptance of null hypothesis, which is actually false.
  • When the null hypothesis is true and you reject it, you make a type I error. The probability of making a type I error is α, which is the level of significance you set for your hypothesis test. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis.
  • When the null hypothesis is false and you fail to reject it, you make a type II error. The probability of making a type II error is β, which depends on the power of the test.
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