This post will provide some basic ideas for developing experiments. The process of doing valid experiments is rather challenging as one misstep can make your results invalid. Therefore, care is needed when attempting to set up an experiment

**Definition**

An experiment is a process in which changes are made to input variables to see how they affect the output variable(s). The inputs are called controllable variables, while the outputs are called response variables. Other variables that cannot be controlled are called uncontrollable variables.

When developing an experiment, the experimenter’s approach or plan for experimenting is called the strategy of experimentation. Extensive planning is necessary to conduct an experiment, while the actual data collection is often not that difficult.

**Best Guess Approach**

There are several different strategies for experimentation. The best-guess approach involves manipulating input variables based on prior results from the output variable. For example, if you are teaching a math class and notice that students score better when they work in groups in the morning compared to working in the afternoon. You may switch to group work in the morning and see if lectures may further increase performance.

This guesswork can be highly efficient if you are familiar with the domain in which you are doing the experiments. However, if the guess is wrong, you have to continue guessing, and this can go on for a long time.

**One-Factor-At-A-Time**

Another strategy of experimentation is the one-factor-at-a-time (OFAT) approach. You begin by having a baseline for each factor (variable) and then vary each variable to see how it affects the output. For example, you can switch whether students study in the morning or even and see how it affects performance. Then you might test whether group work and individual work affect scores.

The biggest weakness with this is that you can see interactions between variables. Interactions are an instance in which one factor does not produce the same results at a different level of another factor. Interactions can be hard to understand, but sometimes when two factors are mapped at the same time with the response variable, the lines cross to indicate that there is an interaction.

**Factorial Experiments**

Factorial experiments involve varying factors together. For example, a 2^2 factorial design means four combinations of experiments with two variables are varied, and one response variable with four possible combinations of experiments. Often these types of experiments are drawn as a square, as shown below.

Each point represents a different combination of the two factors. The calculation of this involves subtracting the means of the variable or factor on the x-axis. If we run each combination twice, we would calculate the difference, as shown below.

The more significant this difference, the more likely there is a strong effect based on the independent variables in the model.

When the number of combinations becomes large and complicated to manage, it may not be practical to run all possible combinations. In this situation, an experimenter will use a fractional factorial experiment in which only some of the combinations are used. For example, if 32 experiments are possible (2^5), maybe only 12 of them are conducted. The calculation is the same as above, just with more groups to compare.

**Conclusion**

Experiments are a practical way to determine the best combination of factors or variables for a given output variable(s). The majority of the time is spent planning and designing the experiment, with the actual data collection being straightforward.