Normal Distribution Calculator

Z-Score: -
P(X < x) - Cumulative: -
P(X > x) - Complementary: -
P(μ-σ < X < μ+σ): 68.27% (Approx)

What is a Normal Distribution Calculator?

A normal distribution calculator online is a statistical tool used to calculate probabilities under the Gaussian distribution (commonly known as the bell curve). This calculator helps researchers, students, and data scientists find the area under the curve for specific values, given a mean and a standard deviation.

The Normal Distribution is symmetrical around the mean, where the data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.

How to Use This Tool Online

Using our calculator is straightforward. Follow these steps to get precise statistical results:

  • Enter the Mean (μ): This is the average value of your dataset. For a standard normal distribution, the mean is 0.
  • Enter the Standard Deviation (σ): This represents the spread or dispersion of your data. For standard normal distribution, this is 1.
  • Enter the Test Value (x): This is the specific point you want to analyze on the X-axis.
  • Click Calculate: The tool will instantly provide the Z-score and the probability values for both lower-tail and upper-tail regions.

Understanding Z-Scores and Probabilities

A Z-score indicates how many standard deviations an element is from the mean. A positive Z-score means the value is above the average, while a negative score indicates it is below. The probabilities calculated (P values) represent the likelihood of a random variable falling within a certain range. For example, P(X < x) shows the cumulative probability from negative infinity up to your test value.

Frequently Asked Questions

What is the 68-95-99.7 rule?
In a normal distribution, approximately 68% of the data falls within one standard deviation of the mean, 95% within two, and 99.7% within three. This is also known as the empirical rule.

Why is the Normal Distribution important?
Many natural phenomena, such as heights, blood pressure, and test scores, follow this pattern. It is also the foundation of the Central Limit Theorem in statistics.