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What is the minimum level of significance at which you would reject the hypothesis?
Wiki User
∙ 14y ago
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.
Wiki User
∙ 14y ago
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Why is the level of significance always small?
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.
In a hypothesis testing the alternative hypothesis is assumed?
No. The null hypothesis is assumed to be correct unless there is sufficient evidence from the sample and the given criteria (significance level) to reject it.
Why is hypothesis could be rejected?
To reject null hypothesis, because there is a very low probability (below the significance level) that the observed values would have been observed if the hypothesis were true.
Which is better a 005 level of significance or 001 level of significance?
"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.
Can you accept a null hypothesis under the t statistic and then reject the same null hypothesis using the F statistic?
At the same level of significance and against the same alternative hypothesis, the two tests are equivalent.
What is the p-value if 0.01 is the level of significance and the mean is 18688 and the standard deviation is 15500?
In order to solve this you need the null hypothesis value also level of significance only helps you decide whether or not to reject the null hypothesis, is the p-value is above this then you do not reject the null hypothesis, if it is below you reject the null hypothesis Level of significance has nothing to do with the math
Why is the level of significance always small?
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.
In a hypothesis testing the alternative hypothesis is assumed?
No. The null hypothesis is assumed to be correct unless there is sufficient evidence from the sample and the given criteria (significance level) to reject it.
Why is hypothesis could be rejected?
To reject null hypothesis, because there is a very low probability (below the significance level) that the observed values would have been observed if the hypothesis were true.
Which is better a 005 level of significance or 001 level of significance?
"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.
Can you accept a null hypothesis under the t statistic and then reject the same null hypothesis using the F statistic?
At the same level of significance and against the same alternative hypothesis, the two tests are equivalent.
What is another name for the probability of observing a sample value at least as extreme as a given on under a null hypothesis?
The significance level of the observation - under the null hypothesis. The significance level of the observation - under the null hypothesis. The significance level of the observation - under the null hypothesis. The significance level of the observation - under the null hypothesis.
What p-values will lead us to reject the null hypothesis if the level of significance equals 0.05?
Any value greater than 0.05
The 5 percent level of significance and 1 percent level are the minimum level for certain kinds of research Anyone know which ones?
I think it is hypothesis testing
If your p-value is .7712 do you reject the hypothesis at 10 percent level of statistical significance If so is there a relationship in the test?
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
When should you accept a null hypothesis?
The null hypothesis cannot be accepted. Statistical tests only check whether differences in means are probably due to chance differences in sampling (the reason variance is so important). So if the p-value obtained by the data is larger than the significance level against which you are testing, we only fail to reject the null. If the p-value is lower than the significance level, the null hypothesis is rejected in favor of the alternative hypothesis.
When you reject the null hypothesis?
You reject the null hypothesis if the probability of the observed outcome, calculated under the null hypothesis, is smaller than some preset level. Commonly used levels are 10%, 5%, 1% or 0.1%.
