Distribution Settings
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σ =
Moments & Percentiles
Results
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Mean
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Variance
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P5 (5th pct.)
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P95 (95th pct.)
PDF & CDF
Monte Carlo Histogram vs Theoretical PDF
Select a distribution type and adjust parameters to plot PDF/PMF and CDF curves in real time. Compute percentiles, mean, and variance for reliability engineering and statistical analysis.
Mean μ slider—you'll see the entire curve shift left or right.Std Dev σ, controls the width of the bell curve? What does a "wider" curve mean in real terms?Std Dev σ in the simulator and watch the bell curve flatten out—that's visualizing increased uncertainty.Trials n), each with a 2% defect probability (Probability p), the Binomial distribution tells you the probability of finding exactly 0, 1, 2,... defective parts. Switch the simulator to "Binomial" and adjust n and p. You'll see a discrete PMF (Probability Mass Function)—a series of bars, not a smooth curve—showing each possible count.The Normal (Gaussian) Distribution is defined by its Probability Density Function (PDF). This continuous function describes the relative likelihood for a continuous random variable to take on a given value.
$$f(x) = \frac{1}{\sigma\sqrt{2\pi}}e^{ -\frac{1}{2}\left( \frac{x - \mu}{\sigma} \right)^2 }$$Here, $x$ is the variable (e.g., measured stress, material strength). $\mu$ is the mean (the central, most probable value). $\sigma$ is the standard deviation , controlling the spread. The area under the PDF curve between two points gives the probability that $x$ falls in that range.
The Binomial Distribution is defined by its Probability Mass Function (PMF). This discrete function gives the probability of getting exactly $k$ successes in $n$ independent trials.
$$P(X = k) = \binom{n}{k}p^k (1-p)^{n-k}$$Here, $n$ is the fixed number of trials , $k$ is the number of successes (from 0 to $n$), and $p$ is the probability of success on a single trial. The term $\binom{n}{k}$ is the binomial coefficient, counting the number of ways to achieve $k$ successes.
Material Property Scatter: The yield strength of a batch of steel isn't a single number. It follows a normal distribution. CAE engineers use this distribution in probabilistic design to ensure a component is reliable not just for the "average" strength, but for the weaker specimens likely to exist.
Monte Carlo Reliability Analysis: Engineers simulate thousands of virtual product tests, each time pulling random input values (like load or friction coefficient) from their defined probability distributions. The output distribution predicts the probability of failure, guiding design improvements.
Quality Control & Defect Prediction: The Binomial distribution is used directly. If a manufacturing process has a known defect rate p, and you sample n parts, this distribution calculates the probability of finding more than a certain number of defects, determining if the batch passes or fails inspection.
Measurement & Sensor Uncertainty: Every physical measurement has error. Repeated measurements of a fixed quantity (like a dimension) will cluster around the true value in a normal distribution. The standard deviation σ quantifies the precision of your measuring tool, which must be accounted for in sensitive CAE validation studies.
First, understand that "the height of the PDF is not the probability itself." For example, the PDF of a normal distribution with mean 0 and standard deviation 0.5 has a high value of about 0.8 at x=0. This means "0 is the most likely outcome," but it does not mean "the probability of X=0 is 0.8." In a continuous distribution, the probability at a single point is always 0. To find a probability, you need to specify an interval and consider the area under the PDF. Try selecting "Normal Distribution" in the tool and visually checking the PDF range from x=0 to 0.5. This area corresponds to the probability that the random variable falls within that interval.
Next, do not confuse the scales when setting distribution parameters. For the binomial distribution's number of trials n and success probability p, and the Poisson distribution's mean occurrence rate λ, you must carefully consider the units to match your real-world data. For instance, if you are modeling "customers arriving at an average rate of 3 per hour" with a Poisson distribution, but your analysis time window is 30 minutes, you must set λ=1.5. In the tool, if you change λ from 3 to 1.5, you'll see the distribution's peak shift to the left. This misconfiguration can severely distort your predictions, so be cautious.
Finally, a practical pitfall: a larger 'N' value in Monte Carlo sampling is not always better. While N=10,000 will indeed produce a histogram closer to the theoretical PDF than N=2,000, you must consider the trade-off between computational cost and accuracy. Especially when evaluating rare "tail risks," simply increasing N is insufficient; you'll need advanced techniques like importance sampling. In the tool, select the "t-distribution (degrees of freedom=3)" and press the "Sample" button multiple times with N=1000. You should see the shape of the histogram's ends (the tails) vary significantly with each run. This illustrates part of the challenge in simulating rare events.
Manufacturing quality control: Component lifespan follows Normal distribution with μ=2000 hours, σ=200 hours. The calculator plots the PDF centered at 2000, computes variance=40000 (hours²), and determines that the 90th percentile is 2256 hours. For a Poisson distribution modeling defects per batch with λ=2.3, the PMF shows peak at mode=2, with variance=2.3 defects². For Binomial with n=100 trials, p=0.05 (5% defect rate), mean=5 defects, variance=4.75, and the 95th percentile reaches 11 defects per 100 units inspected.