Monte Carlo Simulator | Like Oracle Crystal Ball, but without the installation, price or hassle

FREE PLANService will be free (30 simulations/month) for the rest of 2025

CommonAll

Group
Combine multiple distributions simultaneously.
Applied when multiple factors simultaneously impact your result. E.g. revenue = price * quantity or profit = revenue - fixed cost - variable cost.
Normal
Bell-shaped continuous distribution for general applications.
Applied in natural and social sciences to model unknown distributions of real-valued random variables, with applications in statistics, quality control, finance, social analysis, and physical sciences.
Triangular
Continuous distribution with a triangular shape.
Applied in uncertain decision-making scenarios like sales forecasting, simulations as estimates within known bounds, project management, and resource allocation.
Beta
Continuous distribution on the interval [0, 1].
Applied in project management for PERT chart event timing, Bayesian statistics, modeling bounded random behavior, machine learning algorithms requiring random probabilities, and genetics for simulating genetic drift.
BetaPERT
Continuous distribution with shape from expert opinion.
Applied in project management for precise estimates with expert input, risk analysis, decision making, modeling outcome uncertainty across disciplines, and in business for creating plans and forecasting sales.
Gamma
Continuous distribution skewed to the right.
Applid in insurance for claims and premiums modeling, queuing models, reliability engineering for failure times, hydrology for rainfall data, and biology to model time-rated variables like organism lifespans.
Uniform
Continuous distribution with constant probability.
Applied in statistics, simulations, and various fields requiring a constant probability, such as random number generation, modeling equal outcomes, cryptography, computer graphics, and game theory.
Lognormal
Continuous distribution skewed to the right.
Applied in finance for modeling stock prices and property values, scientific fields for time-to-failure predictions, survival analysis, reliability engineering, and income distribution modeling.
Weibull
Continuous distribution for reliability data and survival times.
Applied in reliability engineering, survival analysis, wind speed modeling, material science for material strength, telecommunications for signal strength, hydrology for rainfall data, and engineering for lifetime modeling.
Maximum Extreme
Continuous distribution for modeling extreme events.
Applied in hydrology for flood modeling, finance for extreme market events, environmental science for temperature variations, insurance for risk assessment, and engineering for stress analysis.
Minimum Extreme
Continuous distribution for modeling extreme events.
Applied in hydrology for drought modeling, finance for extreme market events, environmental science for temperature extremes, engineering for material strength, and quality control for minimum life expectancy.
Logistic
Continuous distribution for modeling growth.
Applied in ecology for population growth, statistics for logistic regression, machine learning for binary classification, medicine for growth rates, and economics for logistic growth.
Student's T
Continuous distribution when sample size is small.
Applied in distributions with unknown population standard deviation, in finance for heavy tail modeling, particle physics and in signal processing for noise reduction and machine learning for robust regression.
Exponential
Continuous distribution for modeling time between events.
Applied in reliability engineering for time-to-failure predictions, queuing theory for inter-arrival times, physics for decay rates, telecommunications for call durations, and economics for timing economic shifts.
Pareto
Continuous distribution for modeling inequality.
Applied in economics for wealth distribution modeling, insurance for large claims analysis, quality control for Pareto analysis, networks for connectivity distribution, and human behaviour research.
Binomial
Discrete distribution for # of successes in trials.
Applied in statistics for hypothesis testing, quality control for pass/fail data, marketing for conversion rates, biology for inheritance patterns, and gambling for win/loss outcomes.
Poisson
Discrete distribution for # of events in an interval.
Applied in queuing theory for modeling arrival times, reliability engineering for tracking failures, business for counting calls or customers, biology for mutation rates, and linguistics for analyzing word frequencies.
Hypergeometric
Discrete distribution for successes without replacement.
Applied in statistics for hypothesis testing, ecology for species sampling, quality control for defect sampling, genetics for gene distribution, and game theory for analyzing card games.
Negative Binomial
Discrete distribution for trials until a specified number of failures occur.
Applied in reliability engineering for tests, ecology for species sampling, business for sales quotas, medicine for assessing treatment effectiveness, and quality control for counting defects.
Geometric
Discrete distribution for trials until first success.
Applied in reliability engineering for predicting time-to-failure, queuing theory for trials until success, business for sales to achieve success, biology for genetic inheritance, and for analyzing algorithmic complexity.
Discrete Uniform
Discrete distribution with equal probability on a finite set.
Applied in simulations for random integer generation, games of chance for equal outcomes, business for equitable allocation, computer science for hash functions, and cryptography for secure random generation.
Yes No
Discrete distribution with two outcomes, yes or no.
Applied in decision making for binary outcomes, quality control for pass/fail data, marketing for customer conversion, medicine for treatment outcomes, and social sciences for analyzing survey responses.