The normal distribution is a continuous probability distribution that can be presented on a graph. Continuous probability distributions represent continuous random variables, which can take one or more values. Some examples of continuous variables that can be displayed on a normal distribution graph are:
Achieve better grades quicker with Premium
Geld-zurück-Garantie, wenn du durch die Prüfung fällst
Review generated flashcards
Start learning or create your own AI flashcards
Team Normal Distribution Teachers
Height
Weight
Measurement errors
The bell-shaped curve on the normal distribution graph is symmetrical to the mean, which can be represented by the symbol \(\mu\). The area under the curve is equal to 1. Below is an example of what the normal distribution graph looks like:
Normal distribution graph
The standard deviation tells you how spread out the data are. When this is calculated from the curve above, it can tell you certain things about the data:
68% of the data fall within one standard deviation from the mean, making the probability likely.
95% of the data fall within two standard deviations of the mean, making the probability very likely.
Nearly all the data, 99.7% of the data fall within three standard deviations of the mean, making the probability almost certain.
Standard deviation is a measurement of how spread out the data is, and it can be notated with the symbol \(\sigma\).
When looking at normal distribution, the notation can be written as:
\[X \sim N(\mu, \sigma^2)\]
\(\mu\) = the mean \(\sigma^2\) = the population variance
When you are given this notation, it gives you the information needed to create a normal distribution curve.
The distribution of X is modelled as \(X \sim N(23, 0.25^2)\). Sketch the distribution of X.
To do this, you can start by identifying the mean and the population variance:
\[\mu = 23 \qquad \sigma^2 = 0.25\]
You know that on a normal distribution graph, the curve is symmetrical about the mean, which allows you to draw the bell shape:
Bell curve
The lengths of wingspans are normally distributed with a mean of 22 cm and a standard deviation of 0.4. Sketch the distribution of X.
To do this, you know that the bell-shaped curve will be symmetrical about the mean; therefore, you can sketch the graph as follows:
Worked example
Normal distribution can help you to find probabilities, and to do this you can use the normal cumulative function on your calculator.
Standard normal distribution is a way of standardising normal distribution. It has a mean, \(\mu\), of 0 and a standard deviation, \(\sigma\), of 1. The notation for the standard normal distribution can be written as:
\[Z \sim N(0,1^2)\]
Sign up for free to gain access to all our flashcards.
What is normal distribution?
Normal distribution is a continuous probability distribution that can be presented on a graph.
How do you calculate normal distribution?
To calculate normal distribution you can use the formula, X~N(μ,σ^2), where μ represents the mean and σ^2 represents the population variance.
How do you find exact probability using normal distribution?
To find probabilities using normal distributions you can use the normal cumulative function on your calculator.
At StudySmarter, we have created a learning platform that serves millions of students. Meet the people who work hard to deliver fact based content as well as making sure it is verified.
Lily Hulatt is a Digital Content Specialist with over three years of experience in content strategy and curriculum design. She gained her PhD in English Literature from Durham University in 2022, taught in Durham University’s English Studies Department, and has contributed to a number of publications. Lily specialises in English Literature, English Language, History, and Philosophy.
Get to know Lily
Gabriel Freitas is an AI Engineer with a solid experience in software development, machine learning algorithms, and generative AI, including large language models’ (LLMs) applications. Graduated in Electrical Engineering at the University of São Paulo, he is currently pursuing an MSc in Computer Engineering at the University of Campinas, specializing in machine learning topics. Gabriel has a strong background in software engineering and has worked on projects involving computer vision, embedded AI, and LLM applications.
Get to know Gabriel