Two sample t and z tests are parametric tests used to compare two samples, independent or paired. Run them in Excel using the XLSTAT statistical software.
Two-sample t-test and z-test are popular parametric tests in statistics. These methods are widely used when it comes to inferential statistics and enable to test a general hypothesis called the null hypothesis.
Hypothesis testing is an important concept in statistics. The Z-test and Student's t-test are used to determine the significance level of a set of data. These two tests are used to compare the means of two samples, in other words, they allow us to test the null hypothesis of equality of the means of two groups (samples).
The calculation method differs according to the nature of the samples. A distinction is made between independent samples or paired samples. The t and z tests are known as parametric because the assumption is made that the samples are normally distributed.
XLSTAT provides a complete and flexible two-sample t-tests and z-tests feature that proposes several standard and advanced options that will let you gain a deep insight on your data:
Here are the differences between the two-sample t-test and z-test:
The use of Student's t test requires a decision to be taken beforehand on whether variances of the samples are to be considered equal or not. XLSTAT gives the option of using Fisher's F test to test the null hypothesis of equality of the variances of the groups (samples) and to use the result of the test in the subsequent calculations. If we consider that the two samples have the same variance, the common variance is estimated by:
s² = [(n1-1)s1² + (n2-1)s2²] / (n1 + n2 - 2)
Where s1 is the standard deviation of the first sample (group) and s2 is the standard deviation of the second sample.
The test statistic is therefore given by:
t = (µ1 - µ2 -D) / (s √1/n1 + 1/n2)
The t statistic follows a Student distribution with n1+n2-2 degrees of freedom.
If we consider that the variances are different, the statistic is given by:
t = (µ1 - µ2 -D) / (√s1²/n1 + s2²/n2)
For the z-test, the variance s² of the population is presumed to be known. The user can enter this value or estimate it from the data (this is offered for teaching purposes only). The test statistic is given by:
z = (µ1 - µ2 -D) / (σ √1/n1 + 1/n2)
The z statistic follows a normal distribution.
If two samples are paired, they have to be of the same size. Where values are missing from certain observations, either the observation is removed from both samples or the missing values are estimated.
We study the mean of the calculated differences for the n observations. If d is the mean of the differences, s² the variance of the differences and D the supposed difference, the statistic of the t test is given by:
T= (d-D) ⁄ (s/√n)
The t statistic follows a Student distribution with n-1 degrees of freedom.
For the z test, the statistic is as follows where σ² is the variance
z= (d-D) ⁄ (σ/√n)
The z statistic follows a normal distribution.
Three types of tests are possible, depending on the null hypothesis, and therefore on the alternative hypothesis chosen:
Here are a few tutorials on how to use the Two-sample t-test and z-test feature:
