Bias > Measurement Error

## What is Measurement Error?

Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value. It includes **random error** (naturally occurring errors that are to be expected with any experiment) and** systematic error** (caused by a mis-calibrated instrument that affects *all* measurements).

For example, let’s say you were measuring the weights of 100 marathon athletes. The scale you use is one pound off: this is a **systematic error** that will result in **all **athletes body weight calculations to be off by a pound. On the other hand, let’s say your scale was accurate. Some athletes might be more dehydrated than others. Some might have wetter (and therefore heavier) clothing or a 2 oz. candy bar in a pocket. These are **random errors** and are to be expected. In fact, all collected samples will have random errors — they are, for the most part, unavoidable.

Measurement errors can quickly grow in size when used in formulas. For example, if you’re using a small error in a velocity measurement to calculate kinetic energy, your errors can easily quadruple. To account for this, you should use a formula for error propagation whenever you use uncertain measures in an experiment to calculate something else.

## Different Measures of Error

Different measures of error include:

**Absolute Error:**the amount of error in your measurement. For example, if you step on a scale and it says 150 pounds but you know your true weight is 145 pounds, then the scale has an absolute error of 150 lbs – 145 lbs = 5 lbs.**Greatest Possible Error:**defined as one half of the measuring unit. For example, if you use a ruler that measures in whole yards (i.e. without any fractions), then the greatest possible error is one half yard.**Instrument Error:**error caused by an inaccurate instrument (like a scale that is off or a poorly worded questionnaire).**Margin of Error:**an amount above and below your measurement. For example, you might say that the average baby weighs 8 pounds with a margin of error of 2 pounds (± 2 lbs).**Measurement Location Error**: caused by an instrument being placed somewhere it shouldn’t, like a thermometer left out in the full sun.**Operator Error**: human factors that cause error, like reading a scale incorrectly.**Percent Error**: another way of expressing measurement error. Defined as:

**Relative Error:**the ratio of the absolute error to the accepted measurement. As a formula, that’s:

## Ways to Reduce Measurement Error

- Double check all measurements for accuracy. For example, double-enter all inputs on two worksheets and compare them.
- Double check your formulas are correct.
- Make sure observers and measurement takers are well trained.
- Make the measurement with the instrument that has the highest precision.
- Take the measurements under controlled conditions.
- Pilot test your measuring instruments. For example, put together a focus group and ask how easy or difficult the questions were to understand.
- Use multiple measures for the same construct. For example, if you are testing for depression, use two different questionnaires.

## Statistical Procedures to Assess Measurement Error

The following methods assess “absolute reliability”:

**Standard error of measurement (SEM)**: estimates how repeated measurements taken on the same instrument are estimated around the true score.**Coefficient of variation (CV)**: a measure of the variability of a distribution of repeated scores or measurements. Smaller values indicate a smaller variation and therefore values closer to the true score.**Limits of agreement (LOA)**: gives an estimate of the interval where a proportion of the differences lie between measurements.

**Need help with a homework or test question?** Chegg offers 30 minutes of free tutoring, so you can try them out before committing to a subscription. Click here for more details.

If you prefer an **online interactive environment** to learn R and statistics, this *free R Tutorial by Datacamp* is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try *this Statistics with R track*.

*Facebook page*.