How to calculate signal to noise ratio
In statistics the signal to noise ratio (SNR) is the mean divided by the standard deviation. The mean represents the signal and the standard deviation represents the noise. For a mean of 100 and a standard deviation of 20, the SNR is 100 ÷ 20 = 5 — the signal is five times the size of the noise. The noise term is usually the standard deviation of the measurements, so calculate that before dividing.
How to use this calculator
Enter the mean (the signal) and the standard deviation (the noise). The standard deviation must be non-zero. The result shows the SNR. This is the reciprocal of the coefficient of variation, so a higher SNR means a cleaner, more consistent measurement.
Both inputs must be in the same unit; because they are divided, the result itself is unitless.
Reading SNR values
A higher SNR means the signal stands out clearly above the variability, while a value near 1 means the noise is as large as the signal. The same mean and SD also feed the skewness calculator, and you can estimate SD quickly from the data range with the range rule of thumb calculator.
Frequently asked questions
- How do I calculate signal to noise ratio?
- Divide the mean by the standard deviation. For a mean of 100 and SD of 20, the SNR is 5.
- What does a higher SNR mean?
- A higher ratio means the signal is large relative to the noise, indicating a cleaner, more consistent measurement.
- How does SNR relate to the coefficient of variation?
- The statistical SNR (mean ÷ SD) is the reciprocal of the coefficient of variation (SD ÷ mean).
- Why must the standard deviation be non-zero?
- The formula divides by the standard deviation, so a value of zero would make the ratio undefined.
- Is this the same as SNR in decibels?
- No. This is the statistical SNR, a plain ratio of mean to standard deviation. Engineering SNR compares signal power to noise power and is expressed in decibels as 10·log₁₀(P_signal ÷ P_noise), so the two figures are not interchangeable.