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




-



-X2 



SS 



mean 




ResultsQ
MeanaMeanb
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

MeanaMeanb
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.]


±
±




Home Click this link only if you did not arrive here via the VassarStats main page.





©Richard Lowry 2000-
All rights reserved.