# Random Errors vs. Systematic Errors: The Difference

Often in statistics, researchers must collect data before performing a hypothesis test or calculating a confidence interval.

When collecting data, there are two types of errors that could occur: random errors and systematic errors.

This tutorial provides an explanation of both types of errors along with examples of how each error can occur in different scenarios.

## Random Errors

The first type of errors that can occur during data collection are known as random errors.

These are errors that occur due to random chance for a variety of reasons.

Here are some real-world cases where random errors could occur:

• A botanist measures the height of plants in a particular field and due to wind, occasionally overestimates or underestimates the true height of the plants.
• An electrician measures the voltage of batteries produced by a particular factor, which can experience electronic noise in the circuits that may cause the voltage to be overestimated or underestimated.

Random errors follow a normal distribution, which means that most measurements will likely fall somewhere close to the mean but the standard deviation of the measurements can vary quite a bit.

Random errors are known to affect the precision of estimates.

One way in which the effects of random errors can be minimized is by making repeated measurements during different time periods and taking the average of those measurements.

Depending on the resources and time available for a given study, it may or may not be realistic to spend time taking repeated measurements.

## Systematic Errors

The second type of errors that can occur during data collection are known as systematic errors.

These are errors that occur due to two main reasons:

1. The instrument being used to take measurements is faulty.

For example, suppose an electrician is measuring the voltage of batteries produced in a particular factory and the device that measures the voltage has a short-circuit, causing it to produce inaccurate measurements.

In this scenario, even if the electrician is using the device properly the actual data collected by the device is likely to be wrong.

2. The individual taking the measurements is simply using the instrument wrong.

For example, suppose a botanist is measuring the height of plants in a field but fails to accurately measure the height of the plants starting from the stem.

In this scenario, even if the instrument the botanist uses is working properly the actual data collected by the botanist will be incorrect because she isn’t measuring using a proper technique.

Systematic errors have a direct effect on the accuracy of estimates.

This means that it’s unlikely that a researcher will be able to get an accurate measurement of the mean value of the experimental unit they’re attempting to measure.

The only way to mitigate this type of error is to ensure that the individual collecting the data is properly trained in how to use the measurement equipment and to ensure that the equipment itself is known to be reliable and in working condition.