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

Standard deviation of sample set.

Default: 1

Randomly sampled data from the population.

Hypothesized mean of the population.

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

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 population mean is equal to mean.

Default: mean(pop) != mean

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 population mean. confidence interval indicates the uncertainty in the estimate of the true population mean.

low

Lower limit of the estimate of the population mean.

high

Upper limit of the estimate of the population mean.

Sample statistics of the Z test.

sample mean

Mean of sample set.

sample standard deviation

Standard deviation of sample set.

sample standard error mean

Standard error of the mean of sample set.

sample Z value

Sample test statistic used in the Z test.

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

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(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

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.