# L33 Psych 300 Chapter Notes - Chapter 9: Null Hypothesis, Sampling Distribution, Statistical Hypothesis Testing

by OC2558341

Department

Psychological & Brain Sci (Psychology)Course Code

L33 Psych 300Professor

NestojkoChapter

9This

**preview**shows half of the first page. to view the full**3 pages of the document.**Chapter 09: the Single-Sample t

Test and the Paired-Samples t

Test

●Review: Six Steps of Hypothesis Testing

○Identify the populations, distribution, and assumptions: then choose the

appropriate hypothesis test

○State the null and research hypothesis

○Calculate mean and standard error of the sampling distribution (the comparison

distribution)

○Determine the critical values (cutoffs) that indicate the points beyond which we

will reject the null hypothesis

○Calculate the test statistic

○Decide whether to reject or fail to reject the null hypothesis

○Make a confidence interval (will learn this soon)

○Calculate effect size

●T

-statistic - the distance of a sample mean from a population mean in terms of the

estimated standard error

○We use t

distributions when we do not know the population standard deviation

and are comparing only two groups

○The two groups may be a sample and a population, or two samples as part of a

within-groups design or a between-groups design

○The formula for the t

statistic for a single-sample t

test is the same as the formula

for the z

statistic for a distribution of means, except that we use estimated

standard error in the denominator rather than the actual standard error for the

population

○We calculate estimated standard error by dividing by (N - 1) rather than dividing

by N, when calculating standard error

●T-Test and Z-Test

○If we know the mean and the SD of the population, use a z-test (single-sample

z-test, chapters 6-8)

○But if we don’t know the SD of the population, use a t-test

■Single-sample t-test (if we know the mean)

■Two samples (if we don’t know the mean)

●Within groups design: paired-sample t-test

●Between groups design: independent samples t-test

●How t-tests are different from z-tests:

○In t-tests:

■We will estimate the population SD using our sample data

■The sampling distribution of means will no longer be a normal (z) shape;

it will be a t

distribution shape, and the particular shape will depend on

sample size (N)

■Instead of using a z-table to look up critical cutoff values, we will use a

t-table, and in that tabel the critical cutoff values will depend on the

degrees of freedom (N - 1)

###### You're Reading a Preview

Unlock to view full version