The significance level is always small because significance levels tell you if you can reject the null-hypothesis or if you cannot reject the null-hypothesis in a hypothesis test. The thought behind this is that if your p-value, or the probability of getting a value at least as extreme as the one observed, is smaller than the significance level, then the null hypothesis can be rejected. If the significance level was larger, then statisticians would reject the accuracy of hypotheses without proper reason.
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What is the importance of the level of significance of study findings in a quantitative research report
"Better" is subjective. A 0.005 level of significance refers to a statistical test in which there is only a 0.5 percent chance that a result as extreme as that observed (or more extreme) occurs by pure chance. A 0.001 level of significance is even stricter. So with the 0.001 level of significance, there is a much better chance that when you decide to reject the null hypothesis, it did deserve to be rejected. And consequently the probability that you reject the null hypothesis when it was true (Type I error) is smaller. However, all this comes at a cost. As the level of significance increases, the probability of the Type II error also increases. So, with the 0.001 level of significance, there is a greater probability that you fail to reject the null hypothesis because the evidence against it is not strong enough. So "better" then becomes a consideration of the relative costs and benefits of the consequences of the correct decisions and the two types of errors.
Possibly not - the sample of 60 times is very small.
Yes.
Before conducting a significance test, the statistician will choose an alpha level. Depending upon the severity of having type I or type II error, the statistician will make the alpha level higher or lower. Generally in courts, the alpha level is .05. The other common alpha levels for significance tests are .10 and .01.