Tips for a beginners introduction to statistics and research methodology
Tips for a beginners introduction to statistics and research methodology

I never loved studying statistics and/or research methodology[RM]. Previously, it seemed like both of these subjects somehow didn’t really fit into my interest in exploring psychology, and they also seemed incalculably vast and plain boring. I am a psychology graduate and I finally managed to finish studying these subjects and scrape through with decent marks. But it was only after my graduation time, during the pandemic when I discovered just how easy statistics can be. Funnily enough, all I really needed, was some time away from compulsorily studying those topics in confined ways.

I’m no expert, I’m still learning, here are some tips on how I went about it:

Stats before Research Methodology

If you’re going ahead with wanting to study research methodology in detail, a good place to start would be statistics. Although it isn’t compulsory it’s definitely a great prerequisite to have. Data collection in research methods dips into statistical equations, and formulae, so it’s always good to start from the basics. Here are some of the topics I brushed up in statistics before I decided to study RM

The central limit theorem

Left skewed- Right skewed curves- (understanding in depth what ‘skew’ really means)

Mean, median, mode

Studying the median in detail- ‘Robust statistics’

Inter-quartile range

Confidence levels and intervals

2. Mean, Median, Mode

Starting from scratch can be really overwhelming but that doesn’t mean you can’t at all. It doesn’t take the amount of time you think it would take. Investing time for your basics can go a long way. I started small and figured I’d watch youtube tutorials. Khan Academy did help a lot too. I made a notebook, that would start from the very base of statistics. Mean, median, mode are the three things everyone thinks about when we think of statistics, but knowing these three individually just isn't enough. Although, knowing how these three play together, how they may react differently to graphed data, and why they can help with compiling and analyzing data, might be enough for a good foundation.

3. Dynamic learning

While this is recommended for almost any subject, unfortunately, statistics/ and RM is extremely dry and can get quite tedious to learn. This would mean you could make it enjoyable, make your own examples to understand a concept.

Manipulate data on a graph to see changes, since you would be doing this yourself, you’d remember the specifics of change better than learning it via theory.

An interesting example of manipulating data could be seeing how measures(measures of spread and measures of central tendency) respond to basic mathematical processes like addition and multiplication.

4. Understanding the ideal world

Often, I’ve noticed that all of the rules put forth within the realm of stats or research, all spring from a crafted utopian world without any exceptions. I think all ideas often originate from your own imaginary world, where everything is constant. There aren’t any barriers that limit you from thinking, which would otherwise be quite challenging in the real world. For example- The concept of population and sample. Ideally, you’d want to do research that would have a sample, perfectly representing your population. However, ideally, you could also just use a population because then you’d have all the characteristics you want. The population, of course, would be all the people in the world, suitable for your study. In reality, this wouldn’t be possible. That being said, it’s wonderful how these things start from something seemingly impossible but are still practicable in reality. In this case, you could use the sample of the population for your study. This is how I tried to remember the concepts of population and sample. By working backward, and imagining cases in the imagined ideal world, you could narrow down scenarios to the real world.

5. Understanding it for what it is

Why statistics is actually easy- everything is self-explanatory. A simple example to explain how most concepts are self-explanatory is that of continuous and discrete variables. When I first learnt the types of variables, I couldn’t remember something as basic as continuous and discrete. Then, I realized, the names can be used as simple hacks themselves. Continuous variables are continuous, as close to something arbitrary. The most common example I came across was that of ‘height’ being a continuous variable. An example I used to remember is a ruler. A ruler seems long for the most part, and I just associate that with ‘height’ and then continuous. Remembering discrete was easy, discrete in itself means distinct. For example- a fixed amount of something, that’s specific is discrete. While I could only illustrate with two examples, most concepts in statistics are self-explanatory, you just need to remember them with easy examples.

6. Hypotheses- Type 1 and Type 2 errors

Knowing these two errors, and the difference between them can help. For instance, I made a table to remember these errors, and since I knew this pretty well, I understood p values. Making a table isn’t an out of box idea but it’s simple and easy to revise.

You usually accept a statement if it’s the truth and reject falsities. But what happens when you are deceived? You do the opposite, you reject the truth, accept lies. That’s exactly what type 1 and type 2 errors are.

When I first started learning more about these subjects, I was lost and confused. If you’re trying to learn too, these tips can give you a good head start to the field. Statistics and research are subjects that seem difficult, and too expansive but a good start steers a clear way of understanding important concepts.
['Praharshini Kumar']
['Statistics', 'Research', 'Psychology', 'Beginners Guide']