t-statistics |
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Many statistical tests are reported as Student's t statistics. This table shows the significance-level values for different degrees of freedom (d.f.). Often the reported t-statistics have effectively infinite degrees of freedom and so approximate a unit normal distribution. t-statistics with infinite degrees of freedom are also called z-statistics, paralleling the use of "z" in z-scores.
Table of the two-sided t distribution: |
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Welch's refinement of Student's t-test for possibly unequal variances:
For sample 1 (or estimate 1),
M1 = mean of the sample (or the estimate)
SS1 = sum of squares of observations from the individual sample means
N1 = sample size (or number of observations)
N1 - 1 = degrees of freedom
SS1 / (N1 - 1) = sample variance around the mean (or the measure)
SS1 / ((N1 - 1)(N1)) = standard error variance = EV1 = SE1²
SE1 = Sqrt(EV1) = standard error of the mean (or the standard error of the measure estimate)
(M1 - M2) = difference between means (or estimates)
sqrt (SE1² + SE2²) = joint standard error of the means (or of the estimates
Similarly for sample 2, then
t = (M1 - M2) / sqrt (EV1 + EV2) = (M1 - M2) / sqrt (SE1² + SE2²)
with Welch-Satterthwaite d.f. = (EV1 + EV2)² / (EV1²/ (N1-1) + EV2² /(N2-1))
which is the same as d.f = (SE1² + SE2²)² / (SE14 / (N1-1) + SE24 / (N2-1))
A calculator for this is at https://www.graphpad.com/quickcalcs/ttest1.cfm?Format=SEM
Satterthwaite, F. E. (1946), "An Approximate Distribution of Estimates of Variance Components.", Biometrics Bulletin 2: 110-114
Welch, B. L. (1947), "The generalization of "Student's" problem when several different population variances are involved.", Biometrika 34: 28-35
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