Siddharth Kalla73K reads

It is very important to understand relationship between variables to draw the right conclusion from a statistical analysis. The relationship between variables determines how the right conclusions are reached. Without an understanding of this, you can fall into many pitfalls that accompany statistical analysis and infer wrong results from your data.

Discover 34 more articles on this topic

Don't miss these related articles:

- 1Statistical Hypothesis Testing
- 2Relationships
- 3Correlation
- 4Regression
- 5Student’s T-Test
- 6ANOVA
- 7Nonparametric Statistics
- 8Other Ways to Analyse Data

There are several different kinds of relationships between variables. Before drawing a conclusion, you should first understand how one variable changes with the other. This means you need to establish how the variables are related - is the relationship linear or quadratic or inverse or logarithmic or something else?

Suppose you measure a volume of a gas in a cylinder and measure its pressure. Now you start compressing the gas by pushing a piston all while maintaining the gas at the room temperature. The volume of gas decreases while the pressure increases. You note down different values on a graph paper.

If you take enough measurements, you can see a shape of a parabola defined by xy=constant. This is because gases follow Boyle's law that says when temperature is constant, PV = constant. Here, by taking data you are relating the pressure of the gas with its volume. Similarly, many relationships are linear in nature.

Relationships between variables need to be studied and analyzed before drawing conclusions based on it. In natural science and engineering, this is usually more straightforward as you can keep all parameters except one constant and study how this one parameter affects the result under study.

However, in social sciences, things get much more complicated because parameters may or may not be directly related. There could be a number of indirect consequences and deducing cause and effect can be challenging.

Only when the change in one variable actually causes the change in another parameter is there a causal relationship. Otherwise, it is simply a correlation. Correlation doesn't imply causation. There are ample examples and various types of fallacies in use.

A famous example to prove the point: Increased ice-cream sales shows a strong correlation to deaths by drowning. It would obviously be wrong to conclude that consuming ice-creams causes drowning. The explanation is that more ice-cream gets sold in the summer, when more people go to the beach and other water bodies and therefore increased deaths by drowning.

Correlation between variables can be positive or negative. Positive correlation implies an increase of one quantity causes an increase in the other whereas in negative correlation, an increase in one variable will cause a decrease in the other.

It is important to understand the relationship between variables to draw the right conclusions. Even the best scientists can get this wrong and there are several instances of how studies get correlation and causation mixed up.

Full reference:

Siddharth Kalla (Jul 26, 2011). Relationship Between Variables. Retrieved Aug 08, 2020 from Explorable.com: https://m.explorable.com/relationship-between-variables

The text in this article is licensed under the Creative Commons-License Attribution 4.0 International (CC BY 4.0).

This means you're free to copy, share and adapt any parts (or all) of the text in the article, as long as you give ** appropriate credit** and

That is it. You don't need our permission to copy the article; just include a link/reference back to this page. You can use it freely (with some kind of link), and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations (with clear attribution).

Discover 34 more articles on this topic

Don't miss these related articles:

- 1Statistical Hypothesis Testing
- 2Relationships
- 3Correlation
- 4Regression
- 5Student’s T-Test
- 6ANOVA
- 7Nonparametric Statistics
- 8Other Ways to Analyse Data

Thank you to...

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 827736.

Subscribe / Share

- Subscribe to our RSS Feed
- Like us on Facebook
- Follow us on Twitter
- Founder:
- Oskar Blakstad Blog
- Oskar Blakstad on Twitter

Explorable.com - 2008-2020

You are free to copy, share and adapt any text in the article, as long as you give *appropriate credit* and *provide a link/reference* to this page.