Table of Contents
With the recent advent of digital tools, the rise in data manipulation has become a key challenge. And so, the scientific community has begun taking a more careful look at scientific malpractice involving data manipulation. But why are data so important in scientific research?
Role of data in science
Reliable data facilitates knowledge generation and reproducibility of key scientific protocols and experiments. For each step of a research project, from data collection to knowledge generation, researchers need to pay careful attention to data analysis to ensure that their results are robust.
In science, data are used to confirm or reject a hypothesis, which can fundamentally change the research landscape. Thus, with respect to the outcome of a specific study, data are expected to fit one of two patterns. However, data may not conform to an apparent pattern. When this happens, researchers may engage in malpractices or use unreliable data collection and analysis methods, jeopardising their reputation and career. Hence, it is necessary to resist the temptation to cherry-pick data. Always let the data speak for itself.
There are two ways to ensure the integrity of data and results.
Data validation
Data validation is a streamlined process that ensures the quality and accuracy of collected data. Inaccurate data may keep a researcher from uncovering important discoveries or lead to spurious results. At times, the amount of data collected might help unravel existing patterns that are important.
The data validation process can also provide a glimpse into the patterns within the data, preventing you from forming incorrect hypotheses.
In addition, data validation can also confirm the legitimacy of your study, and help you get a clearer picture of what your study reveals.
Analytical method validation
Analytical method validation confirms that a method is suitable for its intended purpose and will result in high-quality, accurate results.
Often, different analytical methods can produce surprisingly varying results, despite using the same dataset. Therefore, it is necessary to ensure that the methods fit the purpose of your research, a feature referred to as ‘system suitability’. This is one of the main objectives of analytical method validation. The other objective of analytical method validation is ensuring the results’ robustness (ability of your method to provide reliable results under various conditions) and reproducibility (ease with which your work can be repeated in a new setting). Reproducibility is important because it allows other researchers to confirm your findings (which can make your work more impactful) or refute your results if unique conditions in your lab favour one result over others. Moreover, as a collaborative enterprise, scientific research rewards the use and sharing of clearly defined analytical processes.
In the long run, it is rewarding for researchers to double-check their dataset and analytical methods than make the data fit an expected pattern.
Conclusion
While data are the crux of a scientific study, unless it is acquired and validated using the most suitable methods of data and method validation, it may fail to produce authentic and legitimate results. To get useful tips on how to collect and validate data, feel free to approach Elsevier Author Services. Our experts will support you throughout your research journey, ensuring that your results are reproducible, robust, and valid.
