In general, the

**true error**is the difference between the true value of a quantity and the observed measurement (Muth, 2006).

In hypothesis testing, the true error is the error rate of a hypothesis over a whole unknown distribution of examples; It is the probability a single randomly drawn example will be misclassified (Mitchell, 1997).

True error is also sometimes defined as the difference between the true value found by a calculation, and the approximate value found by using a numerical method. As an example, the derivative, which gives a precise value for the slope at a point, can be approximated by the equation f′(x) ≈ (f(x + h) – f(x)) / h; The difference between these two values is the true error.

## What Causes True Error?

True errors can happen because of many reasons, including non-sampling error— a wide range of causes for errors that include:

**Poor data collection methods**(like faulty instruments or inaccurate data recording),**Selection bias**, where your methods for choosing participants are faulty, like the healthy worker effect or**Non response bias**(where individuals don’t want to or can’t respond to a survey).

Increasing the sample size won’t reduce these types of errors and can make them worse (larger samples using the same faulty methods = more errors).

They key to minimizing true error is to make sure your experiment or survey is well designed. You should also make sure your measuring instruments are precise.

## References

Bhattacharyay, S. Errors in Computational Analysis. Retrieved January 6, 2021 from: https://people.uwec.edu/BHATTAS/Errors_in_numer_anal.pdf

Mitchell, T. (1997). Machine Learning. 1st edition. McGraw-Hill.

Muth, J. (2006). Basic Statistics and Pharmaceutical Statistical Applications, Second Edition (Pharmacy Education Series). Chapman and Hall/CRC.