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Can you accept a null hypothesis under the t statistic and then reject the same null hypothesis using the F statistic?
Wiki User
∙ 14y ago
At the same level of significance and against the same alternative hypothesis, the two tests are equivalent.
Wiki User
∙ 14y ago
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How do you perform a Statistical Hypothesis Testing?
To start with you select your hypothesis and its opposite: the null and alternative hypotheses. You select a confidence level (alpha %), which is the probability that your testing procedure rejects the null hypothesis when, if fact, it is true.Next you select a test statistic and calculate its probability distribution under the two hypotheses. You then find the possible values of the test statistic which, if the null hypothesis were true, would only occur alpha % of the times. This is called the critical region.Carry out the trial and collect data. Calculate the value of the test statistic. If it lies in the critical region then you reject the null hypothesis and go with the alternative hypothesis. If the test statistic does not lie in the critical region then you have no evidence to reject the null 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%.
How do you find the test statistic for a mean hypothesis test?
You can calculate a result that is somehow related to the mean, based on the data available. Provided that you can work out its distribution under the null hypothesis against appropriate alternatives, you have a test statistic.
When should i reject a null hypothesis?
When we state that the data analysis suggests that we "Reject the null hypothesis" we are stating that the sample statistic is sufficiently different from our assumed value of the population that it is unlikely to be explained by chance. If we use for example, that under the null hypothesis that engineers make on the average $120,000 per year. If we consider that the test statistic (size n) is normally distributed, we can use a two-tail test with an level of significance "alpha" to identify the lower and upper rejection zones on the normal distributon. If the test statistic falls in the non-rejection zone, we state that the "null hypothesis is not rejected." There are many good websites on hypothesis testing. Wikipedia provides a good summary of controversy on hypothesis testing. I note that some of the controversy stems from the idea that hypothesis testing will prove or validate population parameters, which is really beyond the scope of hypothesis testing theory. http://en.wikipedia.org/wiki/Statistical_hypothesis_testing A second way to determine whether the null hypotheis is to calculate p-values. For this, please see: http://en.wikipedia.org/wiki/P-value
What is Hypothesis Testing of p-value?
The probability of the observed value or something more extreme under the assumption that the null hypothesis is true. That is, the probability of standard scores at least as extreme as the observed test statistic.
How do you perform a Statistical Hypothesis Testing?
To start with you select your hypothesis and its opposite: the null and alternative hypotheses. You select a confidence level (alpha %), which is the probability that your testing procedure rejects the null hypothesis when, if fact, it is true.Next you select a test statistic and calculate its probability distribution under the two hypotheses. You then find the possible values of the test statistic which, if the null hypothesis were true, would only occur alpha % of the times. This is called the critical region.Carry out the trial and collect data. Calculate the value of the test statistic. If it lies in the critical region then you reject the null hypothesis and go with the alternative hypothesis. If the test statistic does not lie in the critical region then you have no evidence to reject the null hypothesis.
What is test statistic. Why do you have to know the distribution of a test statistic?
A test statistic is used to test whether a hypothesis that you have about the underlying distribution of your data is correct or not. The test statistic could be the mean, the variance, the maximum or anything else derived from the observed data. When you know the distribution of the test statistic (under the hypothesis that you want to test) you can find out how probable it was that your test statistic had the value it did have. If this probability is very small, then you reject the hypothesis. The test statistic should be chosen so that under one hypothesis it has one outcome and under the is a summary measure based on the data. It could be the mean, the maximum, the variance or any other statistic. You use a test statistic when you are testing between two hypothesis and the test statistic is one You might think of the test statistic as a single number that summarizes the sample data. Some common test statistics are z-score and t-scores.
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%.
What is the mean of a null hypothesis being rejected?
the hypothesis might be correct* * * * *The available evidence suggests that the observations were less likely to have been obtained from random variables that were distributed according to the null hypothesis than under the alternative hypothesis against which the null was tested.
What is the conclusion if the test statistic is the same as the critical value?
Any decision based on the test statistic is marginal in such a case. It is important to remember that the test statistic is derived on the basis of the null hypothesis and does not make use of the distribution under the alternative hypothesis.
The term statistical significance implies that the results are?
The observed value is unlikely to have occured purely bt chance under the null hypothesis and, as a consequence, you ought to reject the null in favour of the alternative hypothesis.
How do you find the test statistic for a mean hypothesis test?
You can calculate a result that is somehow related to the mean, based on the data available. Provided that you can work out its distribution under the null hypothesis against appropriate alternatives, you have a test statistic.
When should i reject a null hypothesis?
When we state that the data analysis suggests that we "Reject the null hypothesis" we are stating that the sample statistic is sufficiently different from our assumed value of the population that it is unlikely to be explained by chance. If we use for example, that under the null hypothesis that engineers make on the average $120,000 per year. If we consider that the test statistic (size n) is normally distributed, we can use a two-tail test with an level of significance "alpha" to identify the lower and upper rejection zones on the normal distributon. If the test statistic falls in the non-rejection zone, we state that the "null hypothesis is not rejected." There are many good websites on hypothesis testing. Wikipedia provides a good summary of controversy on hypothesis testing. I note that some of the controversy stems from the idea that hypothesis testing will prove or validate population parameters, which is really beyond the scope of hypothesis testing theory. http://en.wikipedia.org/wiki/Statistical_hypothesis_testing A second way to determine whether the null hypotheis is to calculate p-values. For this, please see: http://en.wikipedia.org/wiki/P-value
What is Hypothesis Testing of p-value?
The probability of the observed value or something more extreme under the assumption that the null hypothesis is true. That is, the probability of standard scores at least as extreme as the observed test statistic.
What is the difference between a test statistic and a critical value?
A test statistic is a value calculated from a set of observations. A critical value depends on a null hypothesis about the distribution of the variable and the degree of certainty required from the test. Given a null hypothesis it may be possible to calculate the distribution of the test statistic. Then, given an alternative hypothesis, it is may be possible to calculate the probability of the test statistic taking the observed (or more extreme) value under the null hypothesis and the alternative. Finally, you need the degree of certainty required from the test and this will determine the value such that if the test statistic is more extreme than the critical value, it is unlikely that the observations are consistent with the hypothesis so it must be rejected in favour of the alternative hypothesis. It may not always be possible to calculate the distribution function for the variable.
In a field if statistics what is used to predict or test hypothesis?
A statistical model is fitted to the data. The extent to which the model describes the data can be tested using standard tests - including non-parametric ones. If the model is a good fit then it can be used to make predictions.A hypothesis is tested using a statistic which will be different under the hypothesis being tested and its alternative(s). The procedure is to find the probability distribution of the test statistic under the assumption that the hypothesis being tested is true and then to determine the probability of observing a value at least as extreme as that actually observed.
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.
