Which of the following is not needed to compute a t statistic? A hypothesized va

Question

Which of the following is not needed to compute a t statistic? A hypothesized value for the population mean The value of the population variance or standard deviation The value of the sample mean The value of the sample variance or standard deviation How does sample variance influence the estimated standard error and measures of effect size such as r^2 and Cohen's d? Larger variance increases both the standard error and measures of effect size. Langer variance increases the standard error but decreases measures of effect size. Langer variance decreases the standard error but increases measures of effect size. Langer variance decreases both the standard error and measures of effect size. In general, the large the value of the True False

Explanation / Answer

t statistic=(xbar -Hypothised value of population mean) /{standard deviation of sample/sqrt(sample size)}

Hence the value of population variance or standard deviation is not needed to compute t statistic.

8)Standard error decreases as sample size (n) increases. Hence having more data gives less variation.Less variation means less Standard error and more variation means more standard error

For effect size take r^2 as example

r^2=1-(SSerr/SStot) where SStot is proportional to total sample variance. You see by increasing SStot you are decreasing what is subtracted from 1 to get r^2 which has effwct of increasing r^2.Hence,larger variance increases both standard error and measure of effect size.

9)Larger sample variance make it harder to prove that one sample is significant different to other sample.

Hence, Larger the sample variance less is the chance of rejecting null hypothesis. Hence statement is false

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