You’ve probably heard the old joke about statistics: “Torture the numbers long enough, and they’ll say whatever you want them to say.” Statistics are quoted daily in the news media, yet often these quotes are poorly explained or fail to provide context or analysis. Misrepresentation of statistics is no reason to avoid using statistical analysis in research.
On the contrary, it makes an even stronger argument for including statistical analysis in research and academic studies. High-quality statistical analysis in research is vital to making it clear what the importance of the research is and helping future researchers build on your work. It can also make it easier for laypersons to understand the significance of complex academic research. Let’s talk about what statistical analysis in research is, and explore why it is such a critical tool to help us understand our world better, from science and medicine to humanities and social sciences.
What is Statistical Analysis in Research?
The word statistics, simply put, describes a range of methods for collecting, arranging, analyzing, and reporting quantitative data. Because statistics focuses on quantitative data, data in this case is usually in the form of numbers. So, we can understand statistical analysis in research as a systematic, proven approach to analyzing numerical data so that we can maximize our understanding of what the numbers are telling us. It’s a numerical form of analysis in a research paper.
We use statistical analysis in research for several reasons. The main reason is that while it is often impossible to study all of a target set, it is usually possible to study a small sample of that set. Whether you want to examine humans, animals, plants, rocks, stars, or atoms, a small subset is often all that is available and possible to study within the time and budgetary constraints that researchers must work under. However, the chances that a random sample will be representative of the whole are not high. There is always going to be some variation. How do researchers handle this type of variation when trying to understand data collected from samples? You guessed it -- through statistical analysis. Statistical analysis methods account for variation and differences, and that is why they are so critical to performing high quality research analysis in your paper.
Collecting and analyzing data through statistics is a really important part of furthering our understanding of everything from human behavior to the universe itself. One reason for this is that our assumptions about the world around us are often based on personal experience and anecdotes, but we as individuals lack a broad perspective. It takes time, dedication, and careful work to really understand what is happening in the world around us. Much of what we know and understand about the world today is thanks to hardworking researchers performing statistical analysis in research. Without this research analysis, it would be a lot harder to know what’s going on around us.
Are Statistics Always Useful?
Statistics can be, but are not always, useful. We can determine whether statistics are helpful by looking at the quality of the data gathered and the quality and rigor of the statistical analysis performed. Great statistical analysis is worthless if the data itself is poorly collected or not representative. And even the best data can mislead us when the statistical analysis performed is poor and leads to erroneous results.
It is a sad fact that some writers and researchers who use obscure or inappropriate statistical methodology as a way to get the results they are aiming for, or who fail to provide clear statistical analysis and therefore mislead their readers about the significance of their data. Poor use of statistical methods have led to a replication crisis in science, where the results of many studies cannot be reproduced. Some have argued that this replication crisis is because while collecting data has become much easier in recent decades, scientists and researchers are not adequately trained in statistical analysis in research. The replication crisis has led to falling public trust in science, the consequences of which became sharply clear during the COVID-19 pandemic.
How Can You Improve Statistical Analysis in Your Research?
So how can you avoid misusing data and producing poor quality statistical analysis in your research paper? The first way is to make sure you have a solid understanding of the scientific method. Create a hypothesis, test that hypothesis, and then analyze what happened. Be familiar with the ways that people use and misuse statistical analysis in research to present false conclusions. This will help you improve the quality of your own work and increase your ability to identify and call out poorly performed research analysis in academic papers.
Finally, don’t be afraid to reach out for assistance with your work. You don’t need to spend hours in front of your computer trying to perform statistical analysis with software and statistical analysis methods you don’t fully understand. There are services available to help you turn your data and methods into easy-to-understand results - and they are legitimate services that help researchers perform technical work without interfering their research. These services can do everything from recommending what statistical model you should use to verifying your data. They can also perform full statistical analysis in your research paper for you. Don’t worry about becoming a casualty of poor quality statistical analysis ever again - use the statistical analysis services available to you!
