The Independent-Samples t test

The comparison distribution for an independent-samples t test is a distribution of

differences between means.

When conducting an independent-sample t test because the null hypothesis posits no difference between the population the comparison distribution is a distribution of

differences means with a mean of 0

The comparison distribution is a

distribution of differences between means.

Other things being equal, a larger sample size will _____ effect size

have no effect on

The abbreviated formula for an independent-samples t test is

(Mx -My) / sDifference

Independent sample t.

- tests the hypothesis of differences between two means - There is one independent (IV) with two levels (conditions) - There is one (DV) measured across the conditions -you can conduct multiple t tests, but you increase statistical error.

Which process would generate a distribution of differences between means

A small sample is taken from a population recorded and replaced. A second sample is taken and the difference between the two sample means is recorded this process is repeated many times.

Conceptually, the t test for independent samples makes the same comparisons as the other t tests.

However, the calculations are different, and the critical values are based on degrees of freedom from two samples.

For a two-sample between-groups design the formula for effect size is similar to that for the test statistic in an independent- samples t test except that calculating effect size involves dividing by the

Pooled standard deviation rather than the standard error

In which scenario would an independent-samples t test be appropriate ? Assume that the necessary assumptions would hold

Two sample means need to be compared where each participant belongs to just one of the two samples

In a between-groups design with two groups, the appropriate hypothesis test is a(n):

independent-samples t test

The independent-samples t test

is used to compare two means for a between-groups design, a situation in which each participant is assigned to only one condition

Each end of the confidence interval should be exactly the same distance from the

sample mean

Which of the following will impact the width of the confidence interval for an independent-samples t test?

standard error

When specifying the null and research hypotheses for an independent-samples t test

the research hypothesis posits a difference between population means

Bayesian thinking leads to different conclusions based on

two different priors

When we conduct an independent-samples t test

we cannot calculate individual difference scores. That is why we compare the mean of one sample with the mean of the other sample

If a researcher fails to reject the null hypothesis based on the results of an independent-samples t test to draw the same conclusion based on the confidence interval

zero would have to fall within the bounds of the confidence interval