Tests hypotheses about the mean of two independent populations whose distributions are at least approximately normal and whose variances are known.
Standard deviation of sample set y.
Default: 1
Standard deviation of sample set x.
Default: 1
Sampled data from population x.
Sampled data from population y.
Hypothetical difference between the means of sample set x and sample set y.
Default: 0
Probability that this node incorrectly rejects a true null hypothesis.
Default: 0.05
Error conditions that occur before this node runs.
The node responds to this input according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
Default: No error
Hypothesis to accept if this node rejects the null hypothesis that the two populations have a common mean.
If the null hypothesis is true, the difference between the means of sample set x and sample set y is zero.
Name | Value | Description |
---|---|---|
mean(x) - mean(y) != delta | 0 | The difference between the means of population x and population y is not equal to delta. |
mean(x) - mean(y) > delta | 1 | The difference between the means of population x and population y is greater than delta. |
mean(x) - mean(y) < delta | -1 | The difference between the means of population x and population y is less than delta. |
Default: mean(x) - mean(y) != delta
A Boolean that indicates whether this node rejects the null hypothesis.
True | p value is less than or equal to significance level. This node rejects the null hypothesis and accepts the alternative hypothesis. |
False | p value is greater than significance level. This node accepts the null hypothesis and rejects the alternative hypothesis. |
Smallest significance level that leads to rejection of the null hypothesis based on the sample sets.
Lower and upper limits for the difference between the means of two populations. confidence interval indicates the uncertainty in the estimate of the true difference of means.
Lower limit of the estimate of the difference between the means of two populations.
Upper limit of the estimate of the difference between the means of two populations.
Sample statistics of the Z test.
Mean of sample set x.
Mean of sample set y.
Difference between sample x mean and sample y mean.
Standard deviation of sample set x.
Standard deviation of sample set y.
Standard error of the difference between sample x mean and sample y mean.
Sample test statistic used in the Z test.
sample Z value is equal to $\frac{\mathrm{sample\; mean\; difference}\text{}-\text{}\mathrm{delta}}{\mathrm{sample\; standard\; error\; difference}}$.
Z value that corresponds to significance level and alternative hypothesis.
Algorithm for Calculating Z critical value
Let Z _{ n } represent a Z distributed variate with n degrees of freedom. Z critical value satisfies the following equations based on the value of alternative hypothesis.
alternative hypothesis | Z critical value |
---|---|
mean(x) - mean(y) != delta | Prob{Z _{ n } > Z critical value} = significance level / 2 |
mean(x) - mean(y) > delta | Prob{Z _{ n } > Z critical value} = significance level |
mean(x) - mean(y) < delta | Prob{Z _{ n } > Z critical value} = significance level |
Error information.
The node produces this output according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported
Web Server: Not supported in VIs that run in a web application