Why is reliability important in data collection? What are the main challenges inherent in collecting reliable data?

As we use statistical data to inform our lives and society, we need them to be both accurate and precise. Therefore, collecting quality data is the true challenge and art of producing reliable, constructive statistics.

Keep reading to learn about the importance of reliability in data collection.

## The Value of Reliable Data

The “math part” of statistics is the easy part since we do most statistical analyses on a computer, and the statistics formulas themselves are unchanging and easy to look up. Therefore, once we know enough about statistics to understand which formulas to use and what the resulting statistics mean, the calculations component is simply a matter of plugging data into our chosen equations.

Since statistics themselves are relatively “easy” to calculate, Wheelan explains that well-meaning people produce misleading statistics all the time. He notes that many of the statistics we encounter are mathematically precise (if you repeated your calculations you’d get the same result) but factually inaccurate (even though your numbers are “tight,” they’re wrong). In other words, the numbers hold up to scrutiny but they don’t accurately explain a situation.

For example, you could use statistics to present a compelling link between cold weather and an increase in cold and flu cases. But, if you were to publish your results “proving that the cold causes colds,” you’d be using precise figures to promote inaccurate conclusions because you haven’t even addressed the role of viruses.

Precise but inaccurate statistics happen when our calculations are correct, but the data that went into those calculations were inaccurate, incomplete, or not applicable to our research question.

### Collecting Reliable Data

Obtaining reliable data in the complexity of the real world can be complicated, time-consuming, and expensive.

The challenge of reliability in data collection is present at every level of a research project, from the minuscule details of the study to the overall research question itself. For example, Wheelan explains that even the wording of a survey question can skew the results. In our earlier dog park example, for instance, we could phrase our question as “Do you support the construction of a dog park in town?” or “Do you support a tax increase to fund the construction of a dog park in town?” and get different survey results.

Timing is an additional challenge for medicine and social sciences research, as we’re often interested in outcomes that happen months, years, decades, or even generations after a “treatment” or event. For example, if we were interested in the impact of a mother’s diet during pregnancy on her child’s food allergies, we might have to wait years to collect our data.

Collecting enough data to obtain a reliable dataset can also be expensive. Researchers often have to track randomly selected people down or sort through mountains of literature to obtain the data they are looking for. Provided researchers are not working for free, a commitment to collecting reliable data can add up financially for those funding the research.

The Importance of Reliability in Data Collection￼

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• An explanation and breakdown of statistics into digestible terms
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#### Darya Sinusoid

Darya’s love for reading started with fantasy novels (The LOTR trilogy is still her all-time-favorite). Growing up, however, she found herself transitioning to non-fiction, psychological, and self-help books. She has a degree in Psychology and a deep passion for the subject. She likes reading research-informed books that distill the workings of the human brain/mind/consciousness and thinking of ways to apply the insights to her own life. Some of her favorites include Thinking, Fast and Slow, How We Decide, and The Wisdom of the Enneagram.