Hypothesis Testing (Z Test » Two Samples) (G Dataflow)

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.

error in does not contain an error	error in contains an error
If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

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.

Default: mean(x) - mean(y) != delta

A Boolean that indicates whether this node rejects the null 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.

low

Lower limit of the estimate of the difference between the means of two populations.

high

Upper limit of the estimate of the difference between the means of two populations.

Sample statistics of the Z test.

sample x mean

Mean of sample set x.

sample y mean

Mean of sample set y.

sample mean difference

Difference between sample x mean and sample y mean.

sample x standard deviation

Standard deviation of sample set x.

sample y standard deviation

Standard deviation of sample set y.

sample standard error difference

Standard error of the difference between sample x mean and sample y mean.

sample Z value

Sample test statistic used in the Z test.

sample Z value is equal to $\frac{\mathrm{sample\; mean\; difference}-\mathrm{delta}}{\mathrm{sample\; standard\; error\; difference}}$.

Z critical value

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{Zn > Z critical value} = significance level / 2
mean(x) - mean(y) > delta	Prob{Zn > Z critical value} = significance level
mean(x) - mean(y) < delta	Prob{Zn > 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.

error in does not contain an error	error in contains an error
If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.