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What is the meaning of Sampling distribution of the test statistic?
Given any sample size there are many samples of that size that can be drawn from the population. In the population is N and the sample size in n, then there are NCn, but remember that the population can be infinite. A test statistic is a value that is calculated from only the observations in a sample (no unknown parameters are estimated). The value of the test statistic will change from sample to sample. The sampling distribution of a test statistic is the probability distribution function for all the values that the test statistic can take across all possible samples.
If you select a large enough sample size can you reject any null hypothesis?
The simple answer is no. This depends on a lot of factors such as alpha which determines the critical value and the absolute value of the difference between the claim and sample data. Mathematically speaking, all things being equal, the larger the sample size the larger the absolute value of the test statistic. The formula for the test statistic mean with sigma known is shown below. You can substitute values in and perform the mathematics. The larger the sample size, the larger the Z value; but note if the numerator is small, even a small denominator will not produce a large Z value. In fact, the numerator could be zero which would make the test statistic zero. Z = (Xbar - μxbar)/(σ/√n) (formula from Elementary Statistics by Mario F. Triola)
The s2 statistic is used to test to test whether the assumption of normality is reasonable for a given population distribution The sample consists of 5000 observations and is divided into 6 category?
The s2 statistic is used to test to test whether the assumption of normality is reasonable for a given population distribution. The sample consists of 5000 observations and is divided into 6 categories (intervals). The degrees of freedom for the chi-square statistic is:
Why would you use a z test rather than a t test?
When the sample size is greater than 30
You are using the t distribution to estimate or test the mean of a sample from a single population If the sample size is 25 then the degrees of freedom are?
There are 24 df.
What is the meaning of Sampling distribution of the test statistic?
Given any sample size there are many samples of that size that can be drawn from the population. In the population is N and the sample size in n, then there are NCn, but remember that the population can be infinite. A test statistic is a value that is calculated from only the observations in a sample (no unknown parameters are estimated). The value of the test statistic will change from sample to sample. The sampling distribution of a test statistic is the probability distribution function for all the values that the test statistic can take across all possible samples.
What is the difference between 2 sample z test statistic and 2 sample t test statistic?
erwtwertgrtewh
What does the test statistic measure?
The test statistic is a measure of how close the sample proportion is to the null value.
When using a one-tailed test and a sample size of 24 what is the probability of a value being to the left of a number that has a student-t statistic of -2.50?
26.5
What are t test and F test In which situation these are applied?
Both are parametric test. The t-test uses a test statistic that is related to the sample mean(s) and is used to compare that with the mean of another sample or some population. The F-test uses a test statistic that is related to the sample variance and is used to compare that with the variance of another sample or some population. Both tests require identical independently distributed random variables. This ensures that the relevant test statistics are approximately normally distributed.
What happens to the crtitical value for a chi square test if the size of the sample is increased?
The size of the sample should not affect the critical value.
If you select a large enough sample size can you reject any null hypothesis?
The simple answer is no. This depends on a lot of factors such as alpha which determines the critical value and the absolute value of the difference between the claim and sample data. Mathematically speaking, all things being equal, the larger the sample size the larger the absolute value of the test statistic. The formula for the test statistic mean with sigma known is shown below. You can substitute values in and perform the mathematics. The larger the sample size, the larger the Z value; but note if the numerator is small, even a small denominator will not produce a large Z value. In fact, the numerator could be zero which would make the test statistic zero. Z = (Xbar - μxbar)/(σ/√n) (formula from Elementary Statistics by Mario F. Triola)
The s2 statistic is used to test to test whether the assumption of normality is reasonable for a given population distribution The sample consists of 5000 observations and is divided into 6 category?
The s2 statistic is used to test to test whether the assumption of normality is reasonable for a given population distribution. The sample consists of 5000 observations and is divided into 6 categories (intervals). The degrees of freedom for the chi-square statistic is:
What is the critical value of an inferential statistic and how does it relate to the alpha level?
For an inferential statistic such as a one-sided t, an F or a chi-square test, a critical value is the number above which a fraction of the values of the inference statistics equal to the alpha level would fall on repeated trials if the null hypothesis were true. For example, suppose the research has chosen an alpha level of 0.05. She has a sample size of 11 and will be using a one-sided t-statistic because she is interested in deciding whether the mean of the population from which she has drawn her sample exceeds a certain given value. The critical value for a t-test in this situation is about 1.8 because about 0.05 of the time anyone could take a sample of size 11 from a population with a known mean and find that the t-statistic calculated for the sample exceeds 1.8.
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.
Why would you use a t test rather than a z test?
When the sample size is greater than 30
Why would you use a z test rather than a t test?
When the sample size is greater than 30
