u reject if P-Value is < significance level. so since .7712 > .10 u fail to reject! remember this: "if P is high Ho will fly nd if P is low Ho must go" Help by USman Noor
I think it is hypothesis testing
"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.
The force and the gravity both 50 persent and another 50 percent that eqels 100 percent
It is usually chosen as 0.05 or 0.01. So, the answer is 0.01 or 1 percent. One can choose a lower level if they want to risking the consequnce.
95 percent is, statistically, 2 sigma.
The significance of the ninety percent contour is to determine the probability of finding the most electrons within a region.
According to the National Statistical Coordination Board, 26.9 percent of Filipino families are poor.
I believe you are asking about hypothesis testing, where we choose an alpha value, (also called a signifance level). Thus, I will rephrase your question as follows: If I choose an alpha value of 0.01, what percent of time do you expect the come to an erroneous conclusion, that is test statistic to fall out of the critical region yet the null hypothesis is true? The answer is 1% of the time, an incorrect rejection of the null hypotheis, which is a type I error.
I have always been careless about the use of the terms "significance level" and "confidence level", in the sense of whether I say I am using a 5% significance level or a 5% confidence level in a statistical test. I would use either one in conversation to mean that if the test were repeated 100 times, my best estimate would be that the test would wrongly reject the null hypothesis 5 times even if the null hypothesis were true. (On the other hand, a 95% confidence interval would be one which we'd expect to contain the true level with probability .95.) I see, though, that web definitions always would have me say that I reject the null at the 5% significance level or with a 95% confidence level. Dismayed, I tried looking up economics articles to see if my usage was entirely idiosyncratic. I found that I was half wrong. Searching over the American Economic Review for 1980-2003 for "5-percent confidence level" and similar terms, I found: 2 cases of 95-percent significance level 27 cases of 5% significance level 4 cases of 10% confidence level 6 cases of 90% confidence level Thus, the web definition is what economists use about 97% of the time for significance level, and about 60% of the time for confidence level. Moreover, most economists use "significance level" for tests, not "confidence level".
it would be with a level of significance of 0.15.
There is no answer , -that is an impossible question.
The percent yield of a reaction measures the efficiency of a reaction. The relationship of the actual yield to the theoretical yield is used to determine this.