t-Test for Independent or Correlated SamplesT
[Traducción en español]

The logic and computational details of two-sample t-tests are described in Chapters 9-12 of the online text Concepts & Applications of Inferential Statistics. For the independent-samples t-test, this page will perform both the "usual" t-test, which assumes that the two samples have equal variances, and the alternative t-test, which assumes that the two samples have unequal variances. (A good formulaic summary of the unequal-variances t-test can be found on the StatsDirect web site. A more thorough account appears in the online journal Behavioral Ecology.)

Setup
 Procedure
Q
Q
Data Entry
Sample A
Sample B
Please be sure to perform
the Data Check procedure.
   
Data Summary
  A
B
Total
n 



-X 



-X2 



SS 



mean 



ResultsQ
Meana—Meanb
t
df



 P 
one-tailed

two-tailed

For independent samples, these results pertain to the "usual" t-test,
which assumes that the two samples have equal variances.
F-Test for the Significance of the Difference
between the Variances of the Two SamplesQ
df1
df2
F
P




[Applicable only to independent samples.]
P>.05 indicates no significant difference detected
between the variances of the two samples.
t-Test Assuming Unequal Sample Variances
[Applicable only to independent samples.]Q
Meana—Meanb
t
df



 P 
one-tailed

two-tailed


Observed
 		Confidence Intervals
0.95
 		0.99
Meana
±
 		±
Meanb
±
 		±
Meana−Meanb
[Assuming equal
sample variances.]

 		±
 		±
Meana−Meanb
[Assuming unequal
sample variances.]

 		±
 		±



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