[We often see study results that seem illogical and contradict those that preceded it in the field. That may well be because they we not conducted properly. This article is intended to help researchers, but serves to let us know where the pitfalls are. MW]
How to Make Your Results More Reproducible
Getting reproducible results is important with any scientific research, whether it’s studying the efficacy of a vaccine or looking at how different chemicals react. According to research, around three-quarters of all study results of irreproducible, making many of them are waste of time.
Maybe using the term ‘a waste of time’ is a little extreme! However, producing results that cannot be repeated tends to cause scientists to question the reliability and accuracy of the experiment.
Some of the most large-scale studies have produced results that are almost impossible to obtain again in the future. Of course, this doesn’t necessarily mean that the results are useless or wrong but it does decrease the reliability of these results.
Reproducibility is important in any kind of scientific experiment, no matter what the field of study. When studies can be repeated and similar results can be obtained, trust and credibility increase.
What is Reproducibility?
Reproducibility refers to the reliability of results. It indicates how likely it is that the same results will be obtained if the experiment was repeated in the future using the exact same methods.
When an experiment is repeated, the same outcome should be obtained. Otherwise, scientists question how good the research method was in the first place and whether or not the results can be trusted.
Having reproducible results is important in any scientific experiment. For experimental results to be published in a credible journal or research paper, they must display reproducibility.
How Can You Increase the Reproducibility of Results?
Here are some top tips to help you to enhance the reproducibility of research data (SciNote) so that you can make your research more impactful and reliable.
Don’t Skip the Numerical Values
Displaying your results using graphs and tables is a great way to highlight trends and patterns in data sets. However, it’s important not to omit the numerical values when you are showcasing your results.
Quantitative analysis is only possible when exact numbers and values are included in your published data. So, if you want your research to be reproducible, you need to include the numbers!
To avoid your numerical values from cluttering up your graphs and tables, you can include all of the numbers at the end of your report in the supporting information section. You can highlight any anomalies and outliers in this section too.
Include Error Bars On Your Data
Most of the data that you gather during a scientific experiment will have some margin of error. Including error bars on your graphs indicates the amount of variability that researchers can expect if they repeat the experiment in the future.
Your error bars can be used to display the uncertainty so that researchers don’t assume that the data points are one-offs. However, if you have repeated an experiment more than once and have obtained slightly different results, this can be displayed through your error bars.
Show Your Calibration Testing and Use of Standard Materials
With any type of scientific experiment, it’s important to report any of the validation or calibration testing that relates to the experiment.
You should include the standard materials that were used, and note the concentrations of any substances. By showing data from calibration or validation tests, future researchers will be able to replicate the experiment exactly by using the same concentrations and materials.
In many fields, standards have been pre-determined and widely accepted. In this case, you should report your results according to this standard. If there are currently no standards, displaying your materials will make it easier to reproduce the experiment again and again.
Automate Data Analysis
Automation enhances reproducibility in all areas of your research but it can be particularly effective for the data analysis stage.
Consider how the average data points are calculated, how the data is processed, and how it is interpreted to draw conclusions. Each of these steps can affect the outcome of the data analysis and the reproducibility of your experiment.
To ensure reproducibility, automate your data analysis. Use software and artificial intelligence to analyze all of the quantitative and qualitative data that you’ve gathered.
You can standardize your automation process by writing coding scripts that can be used. by future researchers. Make sure to document all of the steps that you take to automate your research and data analysis.
Automation also increases the efficiency of your research process. It can be used to perform iterative, time-consuming data analysis tasks so that your team can spend their time doing more complex tasks in the lab.