Statistics is collecting, analysing, interpreting, and presenting data that you have collected throughout the years. This is crucial because it allows us to understand the study more deeply and assess it critically while adding credibility to how you validated your hypothesis. The development of statistics goes beyond the 17th century. It was inspired by the need to make sense of a large amount of data. Today, the field of statistics is armed with more sophisticated tools that we can apply to almost all scenarios to analyse our research data. Since statistics is highly interdisciplinary, you can apply these statistical tools for any field of study. For example, statistical tools are used in biosciences, engineering, economics, and even in political science. The field of statistics is quite exciting because it uses numerical evidence or data to draw meaningful conclusions. So, let me introduce you to some very basic concepts in statistics.
First, you need to form a valid hypothesis and test the hypothesis by conducting experiments. This is the point where you start accumulating data. In statistics, we use two types of data: primary and secondary. As researchers, we spend hours in our labs doing experiments to collect primary data. Secondary data is data gathered from external studies/sources or experiments that were conducted by other people or researchers. Typically, researchers use both primary and secondary data in statistics as statistical methods are applicable for both types of data (primary and secondary data have their pros and cons, we will discuss those later!). Then comes the fun part where you analyse and interpret your data (well, at least for statisticians). At this point, statistical significance plays an important role in making inferences and drawing conclusions based on your initial hypothesis. This is especially important when applied to a large sample/data set. So, correctly choosing samples is equally important as selecting the right number of replicates/trials, you will realize the importance once you start analysing your samples. You will also notice uncertainties and variations; which are particularly inevitable in data based on biological experiments. In these situations, statistical knowledge is crucial to help you decide on proper methods and analytical tools to deal with these uncertainties and data variations and produce meaningful results.
So, if you ever felt exhausted and stressed out by the idea of applying statistics to your research, let me tell you something. You are not alone! Students experience some form of anxiety when it comes to applying statistical tools for their research. But the good news is that I have some suggestions for you to try to avoid or minimize your statistics anxiety. Firstly, you can refer to similar literature and familiarize yourself with the methods and analysis used in the previous researches and apply it to your study with necessary adjustments. However, this is valid only when your research objectives closely align with the study you are using as a reference for statistical analysis methods. Secondly, you can study statistical tools by yourself and decide which methods would fit best for your research study. There are many fantastic online tutorials and information sources on statistical tools that can be applied for your research. In the third place, you can always get the help of an expert in the field. I know this sounds trivial but, many of us hesitate to do so. I would recommend and emphasize that this is an effective way of getting things done. Therefore, do not be reluctant to reach out to your friends, your professor, or any other expert in the field.
I hope you will have fun with statistics and learn it without hesitation.
Some online platforms to get yourself familiarized with statistical concepts:
This is a good source of statistical concepts.
Statistics for biology
This is a website where you can find a variety of statistical methods.