Table Of Contents

Hypothesis Testing (Z Test » Single Sample) (G Dataflow)

Version:
Last Modified: December 18, 2017

Tests hypotheses about the mean of a population whose distribution is at least approximately normal and whose variance is known.

connector_pane_image
datatype_icon

standard deviation

Standard deviation of sample set.

Default: 1

datatype_icon

sample set

Randomly sampled data from the population.

datatype_icon

mean

Hypothesized mean of the population.

The null hypothesis is that the population mean is equal to mean.

Default: 0

datatype_icon

significance level

Probability that this node incorrectly rejects a true null hypothesis.

Default: 0.05

datatype_icon

error in

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

datatype_icon

alternative hypothesis

Hypothesis to accept if this node rejects the null hypothesis that the population mean is equal to mean.

Name Value Description
mean(pop) != mean 0 The population mean is not equal to mean.
mean(pop) > mean 1 The population mean is greater than mean.
mean(pop) < mean -1 The population mean is less than mean.

Default: mean(pop) != mean

datatype_icon

null hypothesis rejected?

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.
datatype_icon

p value

Smallest significance level that leads to rejection of the null hypothesis based on the sample sets.

datatype_icon

confidence interval

Lower and upper limits for the population mean. confidence interval indicates the uncertainty in the estimate of the true population mean.

datatype_icon

low

Lower limit of the estimate of the population mean.

datatype_icon

high

Upper limit of the estimate of the population mean.

datatype_icon

Z test information

Sample statistics of the Z test.

datatype_icon

sample mean

Mean of sample set.

datatype_icon

sample standard deviation

Standard deviation of sample set.

datatype_icon

sample standard error mean

Standard error of the mean of sample set.

datatype_icon

sample Z value

Sample test statistic used in the Z test.

sample Z value is equal to sample mean mean sample standard error mean .

datatype_icon

Z critical value

Z value that corresponds to significance level and alternative hypothesis.

Algorithm for Calculating Z critical value

Let Zn 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(pop) != mean Prob{Zn > Z critical value} = significance level / 2
mean(pop) > mean Prob{Zn > Z critical value} = significance level
mean(pop) < mean Prob{Zn > Z critical value} = significance level
datatype_icon

error out

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.

Where This Node Can Run:

Desktop OS: Windows

FPGA: This product does not support FPGA devices

Web Server: Not supported in VIs that run in a web application


Recently Viewed Topics