How do I know when to use the independent or dependent samples t test?
Dependent samples are paired measurements for one set of items. Independent samples are measurements made on two different sets of items. When you conduct a hypothesis test using two random samples, you must choose the type of test based on whether the samples are dependent or independent.
What is an example of an independent t test?
For example, you could use an independent t-test to understand whether first year graduate salaries differed based on gender (i.e., your dependent variable would be “first year graduate salaries” and your independent variable would be “gender”, which has two groups: “male” and “female”).
How do you explain independent t-test?
The independent t-test, also called the two sample t-test, independent-samples t-test or student’s t-test, is an inferential statistical test that determines whether there is a statistically significant difference between the means in two unrelated groups.
How do I interpret t-test results in SPSS?
Doing the T-Test Procedure in SPSS To interpret the t-test results, all you need to find on the output is the p-value for the test. To do an hypothesis test at a specific alpha (significance) level, just compare the p-value on the output (labeled as a “Sig.” value on the SPSS output) to the chosen alpha level.
Which SPSS test is for gender?
A chi-square test can answer your hypothesis. This test is used when you want to see if there is a relationship between two categorical variables. For example, if you aim to investigate the relationship between gender (male and female) and socio-economic status you can apply this test.
How can you tell the difference between an independent sample and a matched pair?
The opposite of a matched sample is an independent sample, which deals with unrelated groups. While matched pairs are chosen deliberately, independent samples are usually chosen randomly (through simple random sampling or a similar technique).
What are the assumptions of an independent t-test?
The common assumptions made when doing a t-test include those regarding the scale of measurement, random sampling, normality of data distribution, adequacy of sample size, and equality of variance in standard deviation.