We like to believe that our measuring apparatus are perfect, but the sad fact is that they are not. Several types of errors can occur during your experiments which can affect the way you interpret the results. These include systematic and random errors. Here we will go through how to distinguish between the two types of errors and some important concepts that will help you understand their effects on your results, such as accuracy and precision.

Systematic error

Systematic errors are errors that cause your measurement to shift from the true value by the same amount every time. These errors often arise from faulty or poorly calibrated equipment. They can also be caused by human error if the person conducting the experiment makes the same mistake each time he takes a measurement.

There are two main types of systematic error:

1. Zero error – Measurement instruments in the lab have a zero function. In the case of weighing scales, it allows you to set the weight of the container you place the substance into 0g, such that you only measure the object or substance of interest. If your instrument does not actually set the weight of the container to 0g (e.g. 1g), your measurements will all be off by the weight your container is set to (e.g. +1g).

2. Scale error – This occurs when an instrument is poorly calibrated. If you encounter this error, all your results would be offset by the same fraction.

Systematic errors affect the accuracy of your results. Accuracy of a measurement refers to how close an experimental measurement is to the quantity’s true value.

Random error

Random errors are errors that shift your experimental measurement by a random amount each time. These can occur due to random fluctuations in experimental conditions or poor measurement practices on the researcher’s part. This kind of error often causes replicate results to have a normal distribution, as the measurements are centred around the true value. Here, the mean value is usually the best estimate of the true value, though it may not be the actual true value.

Some examples of random error include:

1. Reaction time – If your experiment involves timing with a stopwatch for example, the speed at which you stop the timing may affect how close to the true value the experimental measurement is. As you may have different reaction times with each round of the experiment, this is a random error.

2. Rounding error – If you were to use an instrument with low precision, rounding off the values may result in random error. Consider if you used a ruler with divisions of 0.1cm to measure the length objects. If the true length of an object is 2.57cm, you may measure it as 2.6cm, +0.03cm of the true value. Whereas if the true length is 2.52cm, you may measure it as 2.5cm, -0.02cm of the true value.

Random error affects the precision or reliability of your results. Precision refers to how close measurements are to one another; i.e. how consistent your measurements are. This has no bearing on the accuracy, i.e. how close your results are to the true value.

Systematic or random?

Sometimes certain errors can be considered systematic and random errors depending on the circumstances – for example, parallax error. If you read all the measurements from the same angle, it would be more likely that you would experience systematic error, as the shift in value would be the same each time. However, if you were to read the measurement from random angles each time, the error would be random.

Differentiating the errors is sometimes straightforward, but can at times be more nuanced. Still, it becomes easier with practice. If you need additional help with these physics concepts or any others, consider engaging us for Physics Tuition to help with your understanding.