t-Test for Independent or Correlated Samples

[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 unit 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
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

For purposes of significance tests and calculation of confidence intervals, values of df associated with the unequal-variance condition are rounded to the nearest integer.
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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