What is the continuous uniform distribution used for?

The continuous uniform distribution models situations where all values in a continuous interval are equally likely: random number generation between bounds, arrival times within a window when exact time is unknown, rounding errors, and baseline probability models for continuous variables.

How do you calculate continuous uniform probability?

The PDF is constant: f(x) = 1/(b-a) for a ≤ x ≤ b. The CDF is linear: F(x) = (x-a)/(b-a). For ranges, P(c ≤ X ≤ d) = (d-c)/(b-a) - simply the length of the interval divided by total range. Probabilities are proportional to interval lengths.

What is the mean of continuous uniform distribution?

The mean equals (a+b)/2, the midpoint of the interval. The variance equals (b-a)²/12. The standard deviation is (b-a)/√12. For example, Uniform(0,10) has mean 5, variance 100/12 ≈ 8.33, and standard deviation ≈ 2.89.

What's the difference between discrete and continuous uniform?

Discrete uniform assigns equal probability to finitely many specific points. Continuous uniform assigns equal probability density over an interval - individual points have probability zero, only intervals have positive probability. One uses PMF, the other uses PDF.

Continuous Uniform Distribution Explorer

Modify Parameters and See Results

Equal probability density over an interval

Parameters

Lower Bound (a): 0.0

Upper Bound (b): 10.0

Lower Bound (a)

0.00

The minimum value of the distribution

Upper Bound (b)

10.00

The maximum value of the distribution

Range (b - a)

10.00

The width of the interval

Density (1/(b-a))

0.1000

Constant probability density over the interval

Statistics

Expected Value

5.0000

Variance

8.3333

Std Deviation

2.8868

Mode

All values in [0, 10]

Probability Calculator

Enter lower bound a:

Enter upper bound b:

Key Properties

Real-World Applications

Random number generation between two values
Arrival time within a time window when all times are equally likely
Random point selection along a line segment
Quantization error in analog-to-digital conversion
Round-off errors in numerical computations

Setting Interval Bounds

Understanding the Flat PDF

Reading the Linear CDF

Computing Interval Probabilities

Using Cumulative Calculators

Range Probability Examples

What is the Continuous Uniform Distribution?

Maximum Entropy Principle

Distribution Statistics

Related Distributions and Tools

Setting Interval Bounds

Adjust a (lower bound) using the top slider to set the minimum value of your interval. The range spans from -20 to 20, accommodating scenarios from symmetric intervals around zero to positive-only ranges.

Set b (upper bound) with the bottom slider to define the maximum value. The calculator automatically ensures b > a, maintaining a valid interval with positive width.

The interval width (b - a) determines both the PDF height 1/(b-a) and the variance. Wider intervals have lower constant density and higher variance.

Understanding the Flat PDF

The PDF displays as a perfectly flat rectangular function with constant height 1/(b-a) across the interval [a, b] and zero outside. This uniform density is the defining feature of continuous uniform distributions.

The rectangular shape represents maximum entropy - complete uncertainty about where within [a, b] values will fall. No subinterval is favored over any other of equal width.

The PDF height adjusts automatically to ensure the total area (probability) equals 1. For interval [0, 10], height is 0.1. For [-5, 5], height is also 0.1 since both have width 10.

Reading the Linear CDF

The CDF rises linearly from 0 to 1 across the interval [a, b]. At any point x in [a, b], F(x) = (x-a)/(b-a) gives the proportion of the interval from a to x.

The perfectly linear rise reflects equal probability density - each unit of width contributes the same probability. For x < a, CDF = 0. For x > b, CDF = 1.

The CDF's slope equals 1/(b-a), matching the PDF height. Steeper slopes (narrower intervals) indicate more concentrated probability, while gentler slopes (wider intervals) show more spread.

Computing Interval Probabilities

For continuous distributions, point probabilities are always zero. Instead, use the range calculator to find P(c ≤ X ≤ d) for any interval [c, d] within [a, b].

The formula is beautifully simple: P(c ≤ X ≤ d) = (d - c)/(b - a). Probability is proportional to interval length - a 2-unit interval has twice the probability of a 1-unit interval.

For boundary options, all four give identical results since individual points have zero probability. The distinction between [c, d], (c, d), [c, d), and (c, d] is meaningless for continuous distributions.

Using Cumulative Calculators

P(X ≤ x) returns (x-a)/(b-a) for a ≤ x ≤ b, giving the fraction of the interval from a to x. For x < a, returns 0. For x ≥ b, returns 1.

P(X ≥ x) computes (b-x)/(b-a), the fraction from x to b. This equals 1 - P(X ≤ x) by the complement rule.

Since P(X = x) = 0, the strict inequalities P(X < x) and P(X > x) give identical results to P(X ≤ x) and P(X ≥ x). This differs fundamentally from discrete distributions.

Range Probability Examples

Example 1: For Uniform[0, 10], what's P(3 ≤ X ≤ 7)? Answer: (7-3)/10 = 0.4 or 40%.

Example 2: For Uniform[-5, 5], what's P(X > 2)? Answer: (5-2)/10 = 0.3 or 30%.

Example 3: For Uniform[2, 8], what's P(X ≤ 5)? Answer: (5-2)/(8-2) = 3/6 = 0.5 or 50%.

The simplicity of these calculations - just ratios of lengths - makes continuous uniform the easiest continuous distribution to work with analytically.

What is the Continuous Uniform Distribution?

The continuous uniform distribution assigns equal probability density to all values in a continuous interval [a, b]. It models complete randomness over a range when no value is more likely than any other.

Two parameters define it: lower bound a and upper bound b. The PDF equals 1/(b-a) on [a, b] and zero elsewhere, ensuring total probability equals 1.

Applications include random number generation, modeling arrival times within known windows, rounding errors, and baseline probability models for continuous variables. For theoretical foundations, see continuous uniform distribution theory page.

Maximum Entropy Principle

Among all continuous distributions on interval [a, b], the uniform distribution has maximum entropy - it contains minimal information and represents maximum uncertainty about where values fall within the interval.

This principle explains why uniform distribution is the default assumption when you know only the support [a, b] and nothing else. It's the "most random" continuous distribution possible for a bounded interval.

When you have no reason to favor any subregion over another, maximum entropy reasoning leads to the uniform distribution. Any deviation from uniformity would imply additional information or structure.

Distribution Statistics

The mean equals (a+b)/2, the midpoint of the interval. For Uniform[0, 10], mean = 5. For Uniform[-3, 7], mean = 2.

The variance equals (b-a)²/12. Wider intervals have proportionally larger variance. For Uniform[0, 10], variance = 100/12 ≈ 8.33.

The standard deviation is (b-a)/√12 ≈ (b-a)/3.46. Perfect symmetry around the midpoint means zero skewness. The distribution has kurtosis of -6/5 = -1.2, indicating lighter tails than normal.

Related Distributions and Tools

The discrete uniform distribution is the discrete analog, assigning equal probability to finitely many specific values rather than equal density over a continuous interval.

The standard uniform distribution Uniform[0, 1] is fundamental to random number generation. All other distributions can be generated by transforming Uniform[0, 1] random variables. The inverse transform method uses F^(-1)(U) where U ~ Uniform[0, 1].

Related Tools:

Discrete Uniform Distribution - Equal probability over finite sets

Standard Uniform Generator - Uniform[0, 1] for simulations

Random Variable Transformation - Generating other distributions from uniform

Beta Distribution Calculator - Generalization of uniform with shape parameters