Normal Distribution Calculator

Calculates the probability or the percentile or the probability between values.

Message

Information

The tool calculates the cumulative distribution (p) or the percentile (π‘₯₁) for the following distributions: Normal distribution, Binomial distribution, T distribution, F distribution, Chi-square distribution, Poisson distribution, Weibull distribution, Exponential distribution.

Choose π‘₯1 to calculate the cumulative probability based on the percentile, or p(X≤π‘₯1) to calculate the percentile based on the cumulative probability π‘₯1, π‘₯2 to calculate p(π‘₯1≤X≤π‘₯2)

Probability density function (PDF)

For a continuous distribution the density is the derivative of the cumulative distribution function.
For example the bell curve line represent the density of the normal distribution.
The area under the density for a specific X range, the integral of the range, is the probability to get value in this range. p(X=π‘₯) = 0.

For a discrete probability distribution the density of value π‘₯ is the probability to get this value: p(X=π‘₯).

Normal distribution

The normal distribution (also known as the Gaussian distribution), is the most widely used in statistical analyses.
This is generally because many natural processes are naturally distributed or have a very similar spread.
Some examples of normally distributed data include height, weight, and error in measurements.

The Normal distribution has a symmetric "Bell Curve" structure. more data exist around the center, which is the average, and as further the value is from the center the less likely it occurs.

Usually, when adding independent random variables, the result tends toward the normal distribution (CLT - The Central Limit Theorem)
You can calculate the values of any normal distribution based on the standard normal distribution (a normal distribution with mean equals zero and standard deviation equals one)
when X distributes normally, μ mean and σ standard deviation, Z=(x-μ)/σ distributes as the standard normal distribution, so you can calculate any normal distribution based on the standard normal distribution.

Normal Distribution

Binomial distribution

The binomial distribution is a discrete distribution, that calculates the probability to get a specific number of successes in an experiment with n trials and p success probability.

When calculating the percentile, there is usually no X that meet the exact probability you enter. The tool will calculate the X that will generate distribution which is equal or bigger than the input probability but will calculate the probabilities for both X and X-1.

When the tool can't calculate the distribution or the density using the binomial distribution, due to a large sample size and/or a large number of successes, it will use the normal approximation with μ = np and σ=√(np(1-p)), or for the percentile calculation it may be a combination between the two distributions using the binomial distribution whenever is possible.
binomial z approximation for binomial


T distribution

The T-student distribution is an artificial distribution used for a normally distributed population, when we don't know the population's standard deviation or when the sample size is too small.

T distribution looks similar to the normal distribution but lower in the middle and with thicker tails. The shape depends on the degrees of freedom, number of independent observations, usually number of observations minus one (n-1). The higher the degree of freedom the more it resembles the normal distribution.

T Distribution (T-student)

Chi-square distribution

The chi-Square distribution is used for a normally distributed population, as an accumulation of independent squared standard normal random variables.

Let Z1, Z2,....Zn be independent standard random variables.
Let X= [Z1^2+ Z2^2+....+Zn^2].
X distributes as a chi-square random variable with n degrees of freedom.

Chi-squared Distribution

F distribution

The F (Fisher Snedecor) distribution is used for a normally distributed population. as a division accumulation of independent squared standard normal random variables, or division between two chi-squared variables.

Let Z1, Z2,....Zn be independent standard random variables.
Let X1 = [Z12 + Z22 +....+ Zn2].

Let Z'1, Z'2,....Z'n be independent standard random variables.
Let X2 = [Z'12 + Z'22 +....+ Z'm2].

Let X = (X1/n) / (x2/m).

X distributes as F random variable with n degrees of freedom (numerator) and m degrees of freedom (denominator)

X1 distribute as a chi-square random variable with n degrees of freedom.
X2 distribute as a chi-square random variable with m degrees of freedom.

Example of use: ANOVA test, F test for variances comparison.

F Distribution (T-student)

Poisson distribution

The poisson distribution is a discrete distribution describe the probability to get the number of events in a fixed unit of time. All the events are independent.
λ is the average number of events per unit of time.
The number of events on t units of time distributes Poisson with average number of events.
The time between events distributes Exponential with mean equals 1/λ.

Exponential distribution

The Exponential distribution is the complementary distribution for the Poisson distribution, it representΧ“ the distribution of the time between events.
A unique character of the distribution is memorylessness - the distribution of the time from now to the next event does not depend on the time you already waited.
Memorylessness example: If the probability for burned-out bulb event in the next 2 months is 0.3, and you waited 1 year without any event, now the probability for an event in the next 2 months is still 0.3.
Example: When the event is a faulty lamp, and the average lamps that need to be replaced in a month are 16.
The number of lamps that need to be replaced in 5 months distributes Pois(80). since: 5 * 16 = 80.
The time between faulty lamp evets distributes Exp(1/16). The unit is months.

Weibull distribution

The Weibull distribution is a continuous distribution that is used for reliability as a lifetime distribution.
When k = 1 (shape), the failure rate is constant. This is the exponential distribution.
When k > 1 (shape), the failure rate increases over time.
p-value calculator