One Sample Proportion Test
Description	A one-sample proportion test is a statistical test used to determine if a single proportion (or percentage) of a population is statistically different from a hypothesized value.
Why to use	To determine if a sample proportion significantly differs from a population proportion or some other known proportion.
When to use	To test whether the probability of occurrence of an event is different from a standard or historical value.	When not to use	When random, normal, and independence conditions for these confidence intervals are not valid.
Prerequisites	Variables must be categorical. The variable is a random sample from the population.
Input	Any dataset.	Output	Level of Significance Z- Status p-value Confidence Interval Alternative Hypothesis Interpretation
Statistical Methods Used	p-value Z-Status	Limitations	The data needs to come from a random sample. The population should follow a binomial distribution.

One Sample Proportion Test is located under Model Studio ( ) in Statistical Analysis below Hypothesis Test, Parametric Test in the left task pane. Use the drag-and-drop method or double-click to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis. You can access it using the search option.

Properties of One Sample Proportion Test

The available properties of the One Sample Proportion Test are shown below.

The table below describes the different properties of the One Sample Proportion Test.

Field		Description	Remark
Task Name		It is the name of the task selected on the workbook canvas.	You can click the text field to edit or modify the task's name. Space between words is not allowed in the Task Name.
Features		It allows you to select the categorical values.	You need to select categorical data. Only columns with categorical data will be shown. You can select only a single categorical value.
Unique Values of Features		It allows you to select the unique values within the features.	Unique values will show you all the unique values present in the selected categorical variable. You can select one unique value from all the values.
Level of Significance		It allows you to set the level of significance.	The default value is 0.05. You can modify this value. The value of alpha lies between 0 and 1.
Hypothesized Proportion		It allows you to set the Hypothesized Value	The default value is 0.5. You can modify this value. The value of alpha lies between 0 and 1.
Advanced	Alternative	Defines the alternative hypothesis.	The default value is Two-sided. You can modify this value. You can choose from – Two-sided - the distributions are not equal Larger- underlying x distribution is stochastically less than the underlying y distribution. Smaller- underlying x distribution is stochastically greater than the underlying y distribution.
Node Configuration		It allows you to select the instance of the AWS server to provide control over the execution of a task in a workbook or workflow.	For more details, refer to Worker Node Configuration.

Example of One Sample Proportion Test

Consider a dataset containing features like Customer Id, Age, Occupation, Current Balance, Current Month Credit, Current Month Debit, Current Month Balance, Location, and so on.
Location is selected as a feature for the categorical variable.

To add a Unique Value of Feature, follow the steps given below.

Click on the Unique Value of Feature in the properties pane. The output Column window is shown.
Click Add all unique value. The unique values within the selected feature are shown in the values drop-down option.
In value, select the required unique value from the drop-down list.
Click Done.

Here, Pune is selected as the Unique Value of the Feature.
Click the vertical ellipsis ( ) and run the node. After the running status is updated click explore to view the test result.
The part of the result page for the One Sample Proportion Test is displayed below.

The Statistical Results section displays One Sample Proportion Test results. It shows the value of the following parameters.

Level of Significance
Z- Status
p-value
Confidence Interval
Alternative Hypothesis
Interpretation

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