How to Avoid Being Tricked By Psychology Jargon

If you have ever tried to read an academic paper or a research study, then you know how tough it can be to decipher what exactly it is trying to say. While some of the field jargon is a necessary part of a professional write up, I do feel that some of the confusing terms lead to the gatekeeping of interesting and valuable information from the general public. And to make matters worse, this complex language has contributed to the spread of misinformation by the media because non-experts are reporting about research studies without understanding what the findings truly mean.

To help you navigate and better understand scientific research, I will be teaching you about the differences between some of the most confusing and misleading words used in most research papers. These words are tricky because they are often treated like synonyms to each other when in reality, they mean completely different things. By understanding the concepts below, you will 1) sound super smart (and who doesn't love that?) and 2) will never be tricked by some fake-and-flashy headline again.

Without further ado, welcome to my little crash course on research terms!

VALIDITY VS. RELIABILITY

The first two topics that often get mixed up are things called "validity" and "reliability." Validity refers to whether the study is actually measuring what it is trying to measure, whereas reliability refers to how consistent the measure is. The nice thing about these characteristics are that they are pretty distinctive and you can likely guess what they mean, but the issue is that they don't always happen at the same time like you may assume.

It is still possible for a study's measure to be super reliable (consistent) and still have low validity because it does not examine what the study claims it is examining. However, it is impossible for a study to have high validity and poor reliability. There are also different types of validity and reliability that can be examined in great detail, but the ultimate goal is to have a study that demonstrates both high general validity and high general reliability.

CORRELATION VS. CAUSATION

I know, these sound a bit more synonymous than the last example, but there is a huge difference between these two relationship types. A correlation (or correlational study) means there is some kind of relationship between two variables, whereas causation (or a causal study) means there is a relationship where one thing CAUSES another thing.

These two types of trends are sometimes problematic in the media because a correlational research study will be advertised as they have proven that Variable X causes Variable Y, when this is not automatically true. A correlation just says that there is some kind of relationship that exists, so this could mean that:

Variable X causes Variable Y
Variable Y causes Variable X
Variable X and Variable Y are both caused by Variable Z

My favorite example of this issue is the following statistic: As the amount of ice cream consumed by the population increases, the number of shark attacks also increases. If this were the headline for a newspaper article, one might immediately think that this means eating ice cream causes you to get bitten by some sweet-toothed shark. But this is a correlational finding, and the truth is that these two things are both caused by the presence of something else entirely: the summer time!

CLINICAL SIGNIFICANCE VS. STATISTICAL SIGNIFICANCE

The last important distinction to know is what we mean by statistical significance and clinical significance. When a study reports that they have achieved statistical significance, it basically means that they were able to mathematically prove their results were not found by chance or random luck. Typically, this is a pretty good sign that a research study's findings are accurate and can be trusted. That being said, there are still ways to inflate the data by messing with the sample size so it can more easily achieve statistical significance.

So then what is clinical significance? Clinical significance is whether or not the treatment/results had a genuine and quantifiable effect (more related to what a person would consider "significant" rather than what an equation would categorize it).

For instance, if you were to read about a new anti-depressant that demonstrated statistically significant results in helping patients feel better, then you would likely be in support of the drug. But when you look deeper into the article, you find that out of the 60 subjects in the sample study, only 5 felt like their mood had improved, you would likely view this drug to be practically useless. Even though the data succeeded in passing the mathematical tests to achieve statistical significance, there is no clinical significance because the drug barely helped anyone.

Hopefully, this helped clarify a thing or two so that you can enjoy reading about scientific research without being misguided or led astray from the truth. If there are any other confusing terms or topics you would like to learn about, leave them down in the comments below!