Data Validity and Data Reliability

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Data quality is paramount in citizen science. Many organisations and researchers have been asking: Can citizens provide data of the same quality as professional scientists? In this step, we’ll find out why data quality matters. We’ll also look at how to manage data quality when you are using sensors, like smartphones, and collecting observations from citizen scientists.

Reliable and valid data

 We can look at data quality in terms of both reliability and validity. Data reliability (or replicability) is about whether you can get the same results when you repeat an experiment or make an observation. For example, let’s say that you see a bird and identify that bird as a robin. At the same time, someone else sees the same bird and comes to the same conclusion. The data is reliable because multiple observations have given the same result.

 Data validity, on the other hand, is about how credible or trustworthy the data are. The data collected are valid if they correctly represent the real world. For example, maybe you measure air quality using a low-cost sensor that has not been calibrated correctly. You may get reliable (consistent) measurements from the sensor but the data will not be valid.