Research key concepts

The independent-samples t-test (or independent t-test, for short) compares the means between two unrelated groups on the same continuous, dependent variable. 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")

The Independent Samples t Test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. The Independent Samples t Test is a parametric test. 

(Explanation: The independent t-test is an inferential test designed to tell us whether we should accept or reject our null hypothesis. You have learned that any two samples from the same population are unlikely to have the same mean. If you carry out an experiment or collect data from two samples because you expect to see a difference between them, you have a problem because there will almost always be some difference due to sampling! It is vital to know whether the difference between the means of your two samples is due to the effect of sampling or to a true difference between the populations they were sampled from.)

Independent sample t-test is used when two separate sets of independent and identically distributed samples are obtained, one from each of the two populations being compared.
The basic formula for the independent sample t-test is:

-hypothesis testing procedure that uses seperate samples for each treatment condition.

-use this test when the population mean and standard deviation are unknown, and 2 seperate groups are being compared.

Independent sample t-test refers to the test used when you want to compare the means on a same, continuous, dependent variable (e.g., the salary of the part-time job for a sophomore relying on gender) for two independent groups (e.g., male and female).

This test is also known as: Independent t - test, Independent measures t - test or Independent two-sample t - test.

A t-test helps you compare whether two groups have different average values (for example, whether men and women have different average heights).

A t-test asks whether a difference between two groups’ averages is unlikely to have occurred because of random chance in sample selection. A difference is more likely to be meaningful and “real” if
(1) the difference between the averages is large,
(2) the sample size is large, and
(3) responses are consistently close to the average values and not widely spread out (the standard deviation is low).

A t-test helps you compare whether two groups have different average values (for example, whether men and women have different average heights).

The independent samples (or two-sample) t-test is used to compare the means of two independent samples.

The independent samples t-test is used to test the hypothesis that the difference between the means of two samples is equal to 0 (this hypothesis is therefore called the null hypothesis).

The independent variable is the major variable which you hope to investigate. It is the variable which is selected, manipulated and measured by the reseacher.