使用可自定义的均值和标准差生成正态分布的随机数。
Center of the distribution
Spread of the distribution
The Gaussian Random Number Generator creates random numbers that follow a normal distribution (also known as a Gaussian distribution or bell curve). Unlike uniform random numbers where all values in a range have equal probability, Gaussian random numbers cluster around a central value (the mean) with decreasing probability as you move away from the mean.
This tool is particularly useful for scientific simulations, statistical analysis, and any application where you need to model natural phenomena that follow a normal distribution.
A normal distribution has these key properties:
The generator uses the Box-Muller transform, a well-established algorithm for generating normally distributed random numbers from uniform random numbers.
Uniform random numbers have equal probability across their range (like rolling a fair die), while Gaussian random numbers cluster around a mean value with decreasing probability as you move away from the mean (like measurement errors or heights in a population).
This generator uses the Box-Muller transform, which produces high-quality normally distributed random numbers. The sample mean and standard deviation shown in the results should be close to your specified values, especially with larger sample sizes.
Yes, a normal distribution can include negative numbers. If your mean is close to zero and your standard deviation is large enough, you'll frequently see negative values. If you need only positive values, you can either set a high mean relative to the standard deviation or filter out negative results.
The histogram visualizes the distribution of the generated numbers. The horizontal axis shows the range of values, and the height of each bar represents how many numbers fall within that range. With enough samples, the histogram should approximate a bell curve shape.
The Gaussian Random Number Generator creates random numbers that follow a normal distribution (also known as a Gaussian distribution or bell curve). Unlike uniform random numbers where all values in a range have equal probability, Gaussian random numbers cluster around a central value (the mean) with decreasing probability as you move away from the mean.
This tool is particularly useful for scientific simulations, statistical analysis, and any application where you need to model natural phenomena that follow a normal distribution.
A normal distribution has these key properties:
The generator uses the Box-Muller transform, a well-established algorithm for generating normally distributed random numbers from uniform random numbers.
Uniform random numbers have equal probability across their range (like rolling a fair die), while Gaussian random numbers cluster around a mean value with decreasing probability as you move away from the mean (like measurement errors or heights in a population).
This generator uses the Box-Muller transform, which produces high-quality normally distributed random numbers. The sample mean and standard deviation shown in the results should be close to your specified values, especially with larger sample sizes.
Yes, a normal distribution can include negative numbers. If your mean is close to zero and your standard deviation is large enough, you'll frequently see negative values. If you need only positive values, you can either set a high mean relative to the standard deviation or filter out negative results.
The histogram visualizes the distribution of the generated numbers. The horizontal axis shows the range of values, and the height of each bar represents how many numbers fall within that range. With enough samples, the histogram should approximate a bell curve shape.