True experiment vs Quasi-experiment: What’s the difference?

There are several different approaches to experiment methodologies. Each is designed to meet the different requirements, scopes, and limitations of the experiment project at hand. Two of the most common experiment types are ‘true’ and ‘quasi.’ But what exactly are these, what purpose do they meet, and what are their differences?

This guide will explore true experiments vs. quasi-experiments and what you need to know if you’re learning about them for the first time.

Why are there different experiment methodologies?

Before we go any further, why are there different experiment types? Because experiment conditions can vary significantly, and some will present challenges. These challenges can affect how you collect data, as well as the data’s robustness, reliability, and quality.

This all comes back to why we run experiments. They examine cause-and-effect relationships and the phenomena that result—and show if these relationships are true or false.

You manipulate one or more variables that might affect the result of your experiment. These are called independent variables. You then have dependent variables, which you measure to check if your manipulation of an independent variable has had an effect. 

These changing variables are key to running experiments effectively and getting reliable, robust data. But you can't manage and control all independent variables.

But before we discuss how experiment design addresses these challenges, let’s explore the baseline type of experiment.

What is a true experiment? 

A true experiment is generally the preferred type of experiment design—you could see it as an experiment standard. This is because you can control all the variables affecting your results. 

You use a control and experiment group to create data that only differs by a specific manipulation. The control group isn't affected by any independent variables, while the experimental group(s) (or treatment group) is. 

This means you can compare the results of your control and experimental groups. Then, you can see the impact of independent variables that define the groups.

In a true experiment, the participants are randomly assigned to either the control or experimental group. This is to produce accurate results that show any cause-and-effect relationships being studied. It also helps to mitigate any bias or other variables that could skew results.

What is a quasi-experiment? 

A quasi-experiment is similar to a true experiment. It aims to establish cause-and-effect relationships between a controlled independent variable and a dependent variable. But in quasi-experiments, you don’t randomly assign control and experimental groups. 

Instead, you assign participants to groups based on predetermined, non-random criteria. This means that control groups aren't always necessary for quasi-experiments. But they're often included in different, non-randomized ways.

A quasi-experiment is an alternative to true experiments when there are practical or ethical reasons, as well as conflicts of interest, why participants can’t be randomly assigned to experiment groups. 

The key difference between true and quasi-experiments 

The key difference between true and quasi-experiments is how you assign participants to treatment groups. For true experiments, you do this randomly. For quasi-experiments, you do this with specific controls in mind.

As a result, quasi-experiment researchers can have more control over the treatment, which is the variable you're manipulating. Again, you might do this for ethical and practical reasons.

Examples of true vs quasi-experiments

Let’s see true and quasi-experiments in action.

True experiment example

You select participants with hay fever for your experiment. Then you randomly assigned them to the treatment and control groups. The control group gets a placebo, whereas the treatment group gets allergy medication. You collect data from both groups and analyze it to see how effective the allergy medication was.

Quasi-experiment example

You want to investigate the effects of a new reading program on literacy development among elementary school students. Instead of randomly assigning students to the program, you compare two existing groups:

• Students from a school that has already implemented the program (treatment group).
• Students from a similar school that uses a traditional reading curriculum (control group).

The groups are naturally formed based on their schools, not randomly assigned. You collect data from both groups and analyze it to assess the impact of the new reading program.

Conclusion

Effective experimental design requires careful consideration when employing the experiment type. Two of these experiment types, true and quasi, both have the same end goal but achieve this in different ways. This is because the random-assignment methodology typical to true experiments isn't suitable for all use cases. 

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