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).

## 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

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.

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