Normal Distribution

Definition and Properties of Normal Distribution

  • Normal distribution, also known as Gaussian distribution, is a type of continuous probability distribution for a real-valued random variable.
  • The graph of the normal distribution is bell-shaped and symmetrical, centered around the mean. The spread is determined by the standard deviation.
  • Normal distribution can be described by two parameters: the mean (μ) and the standard deviation (σ).
  • The total area under the normal distribution graph is equal to 1, corresponding to the total probability of all possible outcomes.

Standard Normal Distribution

  • The standard normal distribution is a specific type of normal distribution where mean (μ) is 0 and standard deviation (σ) is 1.
  • Any normal distribution can be converted to a standard normal distribution using the standardisation formula: Z = (X - μ) / σ, where Z is the standardised value, X is an observation from the original normal distribution, μ and σ are the mean and standard deviation of the original normal distribution respectively.

Using the Normal Distribution Tables

  • Normal distribution tables provide the probabilities for the standard normal distribution, usually the cumulative probability from the mean to Z (Z-table).
  • The cumulative probability from negative infinity to any point Z under the standard normal curve is given by the Z-table.
  • The area under the curve between two values can be found by subtracting the smaller Z-value from the larger Z-value.

Applications of Normal Distribution

  • Normal distribution can be applied in real-world scenarios such as measuring physical characteristics, examining test scores, or investigating environmental data.
  • The Central Limit Theorem states that the sum of many independent and identically distributed random variables tends towards a normal distribution, irrespective of the shape of their individual distributions, provided the expected value and variance are defined and finite.


  • For instance, if you know the average height of a population (μ) and the standard deviation (σ) and want to calculate the probability of randomly selecting someone taller than a certain height (X), the standardisation formula could be used to convert to a Z-score, followed by utilising the Z-table for cumulative probability.
  • Similarly, finding a confidence interval for a certain percentage around the mean can be achieved by referring to the Z-table for corresponding Z-scores and then converting these back to values in terms of the original distribution.