Z Score Calculator

Instructions:

• Enter the Raw Score, Mean (μ), and Standard Deviation (σ) for your data.
• Click "Calculate Z-Score" to calculate the Z-Score and related values.
• Results, including the Z-Score, p-values, and confidence level, will be displayed below.
• Calculation steps will also be shown to explain how the Z-Score was computed.
• A chart visualizes the Z-Score in the context of the normal distribution.
• You can clear the entries, copy the results, and view calculation history.

Calculator

Raw Score:

Mean (μ):

Standard Deviation (σ):

Calculate Z-Score Clear Copy Results

Calculation History

Z-score is a statistical measure that represents the number of standard deviations from the mean. It is used to determine how far a data point is from the mean of a distribution. The Z-score calculator is a tool that helps calculate the Z-score for a given data point.

Concepts

The following concepts are important to understand when working with Z-scores:

Standard Deviation

Standard deviation is a measure of how spread out the data is from the mean. It is calculated by taking the square root of the variance. The variance is calculated by taking the average of the squared differences from the mean.

Normal Distribution

A normal distribution is a bell-shaped curve that represents a set of data that follows a pattern around the mean. The majority of data points are located near the mean, and fewer data points are located farther away from the mean.

Standard Normal Distribution

A standard normal distribution is a normal distribution with a mean of 0 and a standard deviation of 1. It is used to calculate probabilities for any normal distribution.

Z-Score

A Z-score measures how many standard deviations a data point is from the mean. It is calculated by subtracting the mean from the data point and then dividing by the standard deviation.

Formulae

The formula for calculating Z-score is as follows:

Z = (X - μ) / σ

Where:

• Z is the Z-score.
• X is the data point.
• μ is the population mean.
• σ is the population standard deviation.

If you do not know the population values, you can use sample values instead:

Z = (X - x̄) / s

Where:

• x̄ is the sample mean.
• s is the sample standard deviation.

Benefits

The following are some benefits of using Z-scores:

Standardization

Z-scores standardize data by transforming them into units of standard deviations from the mean. This makes it easier to compare data points that have different units or scales.

Outlier Detection

Z-scores can be used to identify outliers in a dataset. Outliers are data points that are significantly different from other data points in the dataset.

Probability Calculation

Z-scores can be used to calculate probabilities for any normal distribution. This makes it easier to determine how likely it is for a particular value to occur in a dataset.

Interesting Facts

Here are some interesting facts about Z-scores:

• A Z-score of 0 indicates that a data point is equal to the mean.
• A positive Z-score indicates that a data point is above the mean.
• A negative Z-score indicates that a data point is below the mean.
• The majority of Z-scores fall between -3 and 3.
• Z-scores can be used to compare data points from different datasets.

Use Cases

Here are some use cases for Z-scores:

Quality Control

Z-scores can be used in quality control to identify products or processes that are outside acceptable limits.

Medical Research

Z-scores can be used in medical research to compare measurements from different populations or groups.

Finance

Z-scores can be used in finance to analyze stock returns and identify outliers.

References

1. Frost, J. (2021). Z-score: Definition, Formula, and Uses. Statistics By Jim.
2. Statology. (2021). 5 Examples of Using Z-Scores in Real Life.