First an F test is performed. If the P value is low (P<0.05) the variances of the two samples cannot be 

assumed to be equal and it should be considered to use the t test with a correction for unequal variances 

(Welch test) (see above). 

The independent samples t test is used to test the hypothesis that the difference between the means of 

two samples is equal to 0 (this hypothesis is therefore called the null hypothesis). The program displays the 

difference between the two means, and the 95% Confidence Interval (CI) of this difference.  Next follow the 

test statistic t, the Degrees of Freedom (DF) and the two tailed probability P.  When the P value is less 

than the conventional 0.05, the null hypothesis is rejected and the conclusion is that the two means do 

indeed differ significantly.   

Log transformation 

If you selected the Log transformation option, the program performs the calculations on the logarithms of 

the observations, but reports the back transformed summary statistics.   

For the t test, the difference and 95% confidence are given, and the test is performed, on the log 

transformed scale. 

Next, the results of the t test are transformed back and the interpretation is as follows: the back 

transformed difference of the means of the logs is the ratio of the geometric means of the two samples 

(see Bland, 2000). 

One sided or two sided tests 

In MedCalc, P values are always two sided (as recommended by Fleiss, 1973, and Altman, 1991) and not 

one sided. 

A two sided (or two tailed) P value is appropriate when the difference between the two means can occur in 

both directions: it may be either negative or positive; the mean of one sample may either be smaller or 

larger than that of the other sample. 

A  one sided test should only be performed when, before the start of the study, it has already been 

established that a difference can only occur in one direction. E.g. when the mean of sample A must be 

more than the mean of sample B for reasons other than those connected with the sample(s).   

Interpretation of P values 

P values should not be interpreted too strictly.  Although a significance level of 5% is generally accepted as 

a cut off point for a significant versus a non significant result, it would be a mistake to interpret a shift of P 

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