What is the difference between a one and two-tailed t-test?
A one-tailed test is used to ascertain if there is any relationship between variables in a single direction, i.e. left or right. As against this, the two-tailed test is used to identify whether or not there is any relationship between variables in either direction.
Why would you use a one-sample t-test?
The one sample t test compares the mean of your sample data to a known value. For example, you might want to know how your sample mean compares to the population mean. You should run a one sample t test when you don’t know the population standard deviation or you have a small sample size.
When should you use a two-sample t test?
The two-sample t-test (Snedecor and Cochran, 1989) is used to determine if two population means are equal. A common application is to test if a new process or treatment is superior to a current process or treatment. There are several variations on this test.
What is the key difference between a one-sample t-test and and independent samples t-test?
Both check to see if a difference between two means is significant. Paired-samples t tests compare scores on two different variables but for the same group of cases; independent-samples t tests compare scores on the same variable but for two different groups of cases.
What is a one sample t-test example?
A one sample test of means compares the mean of a sample to a pre-specified value and tests for a deviation from that value. For example we might know that the average birth weight for white babies in the US is 3,410 grams and wish to compare the average birth weight of a sample of black babies to this value.
What does a 1 sample t test tell you?
The One Sample t Test compares a sample mean to a hypothesized value for the population mean to determine whether the two means are significantly different.
What is the purpose of a two sample t test?
The two-sample t-test allows one to test the null hypothesis that the means of two groups are equal.