What are common sources of error in data collection?

Common sources of error in data collection include human error, sampling bias, measurement inaccuracies, and environmental factors.

Human error is one of the most frequent sources of mistakes in data collection. This can happen when people make simple mistakes, like recording the wrong number or misinterpreting instructions. For example, if you're conducting a survey and accidentally tick the wrong box, the data you collect won't be accurate.

Sampling bias occurs when the sample chosen for data collection isn't representative of the entire population. Imagine you're studying the average height of students in your school but only measure the basketball team. Your results would be skewed because basketball players are generally taller than the average student.

Measurement inaccuracies can also lead to errors. These happen when the tools or methods used to collect data aren't precise. For instance, if you're using a ruler to measure the length of a river but the ruler is bent or broken, your measurements won't be correct. Similarly, if you're using a thermometer that hasn't been calibrated properly, the temperature readings you get will be off.

Environmental factors can affect the accuracy of data collection as well. Weather conditions, for example, can influence the results of outdoor surveys or experiments. If you're measuring wind speed but it's a particularly calm day, your data might not reflect typical conditions. Similarly, if you're collecting water samples from a river after heavy rainfall, the water quality might be different from usual.

Understanding these common sources of error can help you take steps to minimise their impact, ensuring that the data you collect is as accurate and reliable as possible.