Log-Normal Distribution

What is a Log-Normal Distribution?

A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed. This means that if you take the natural logarithm of a variable that has a log-normal distribution, you will get a variable that follows a normal distribution. The log-normal distribution is skewed to the right, unlike the symmetric shape of the normal distribution, and it is bounded below by zero, meaning it does not take negative values.

The log-normal distribution is used to describe processes that are multiplicative in nature, where the overall effect is obtained by multiplying several independent random variables. This is in contrast to the normal distribution, which is typically used for additive processes.

Characteristics of the Log-Normal Distribution

The log-normal distribution is characterized by its non-negative values, which makes it suitable for representing quantities that cannot be negative, such as lengths, areas, weights, and prices of financial assets. It is also characterized by a long right tail, which can account for the occurrence of extreme high values, known as "fat tails".

The log-normal distribution has two parameters: the mean and standard deviation of the underlying normal distribution of the logarithm of the variable. These parameters are not the same as the mean and standard deviation of the log-normal distribution itself, which are typically higher due to the exponential transformation.

Parameter Estimation

To estimate the parameters of a log-normal distribution, one can use the sample mean and standard deviation of the logarithms of the observed data. These estimates can then be used to calculate the parameters of the log-normal distribution itself.

Applications of the Log-Normal Distribution

The log-normal distribution has a wide range of applications across various fields:

  • Finance: In finance, the log-normal distribution is used to model stock prices and other financial assets because prices cannot be negative and often exhibit growth rates that are proportional over time.
  • Environmental Studies: Concentrations of pollutants and other environmental data, which are often multiplicative, are modeled using log-normal distributions.
  • Survival Analysis: In survival analysis, the time until an event, such as failure of a mechanical system or time to death, can be modeled with a log-normal distribution.
  • Pharmacokinetics: The distribution of medication concentrations in the body over time often follows a log-normal distribution.
  • Income Distribution: The distribution of income within a population is sometimes modeled as log-normal, reflecting that income must be positive and the multiplicative effects of compound interest and economic growth.

Probability Density Function

The probability density function (PDF) of a log-normal distribution is given by:

f(x) = (1 / (xσ√(2π))) * exp(- (ln(x) - μ)² / (2σ²))


  • x > 0 is the value of the random variable,
  • μ is the mean of the logarithm of the variable,
  • σ is the standard deviation of the logarithm of the variable, and
  • exp() is the exponential function.

The PDF shows the likelihood of different values of the random variable occurring. For a log-normal distribution, the PDF will peak to the left of the mean and have a long tail extending to the right.

Cumulative Distribution Function

The cumulative distribution function (CDF) of a log-normal distribution is the integral of the PDF and provides the probability that the variable will be less than or equal to a certain value. It is given by the formula:

F(x) = 1/2 + 1/2 * erf((ln(x) - μ) / (σ√2))

where erf() is the error function, which can be computed using numerical methods.


The log-normal distribution is a versatile and widely used distribution in statistics. Its ability to model multiplicative processes and handle non-negative data makes it a powerful tool in various scientific and financial applications. Understanding the properties and applications of the log-normal distribution is important for professionals and researchers who deal with data that exhibit its characteristic skewed, non-negative distribution.

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