Core Concepts for Experimentation

This post will explore several core concepts that are related to experimentation in research. These concepts include

  • Randomization
  • Replication
  • Blocking

Randomization

Randomization involves making sure that the order of the individual runs of the experiment are determined by chance. The main reason for this is to ensure that observations and error are independently distributed random variables themselves. Spreading out all variables in a similar manner helps with the validity of the results. This is because the error is averaged out among all variables and not only one.

Many computer software will automatically randomize the runs of an experiment for you. Such a process helps to eliminate any accidental patterns that may arise if you try to randomize yourself. A common mistake people make when doing experiments is to let convenience determine the run order. For example, if it is hard to set up equipment that can be used as an excuse to run the experiments in a way that is most convenient but may also influence the results.

There are times in which complete randomization is not possible. There are ways to address this statistically, as we will see in the future.

Replication

A replication is a repeated run of a particular factor combination. For example, let say you are looking at the role of gender (two levels) and class level (four levels) affects quiz score. One replication would be to have at least two female freshmen take the quiz.

The benefits of replication include the ability to estimate error and a more precise measurement of the mean for that particular combination of factors.

Another term confused with replication is repeated measurement. They are the same thing with the exception that repeated measurement leaves out randomization. In other words, with replication, the measurement is not consecutive but spread out, while with the repeated measurement, you would measure your variable repeatedly in a row.

Blocking

Blocking is used to improve the measurement accuracy of experiments by blocking the effect of nuisance factors. Nuisance factors are factors we do not care about. For example, if you are trying to assess the impact on quiz scores but do not care whether the quizzes are in the morning or afternoon, you can block for the time of day. You then randomly assign people to each block and rn the experiment.

The goal is to create blocks that are as homogeneous as possible, which means only afternoon people in the pm block and only morning people in the am block. Doing this helps to control for the influence of time of day.

Conclusion

The topics discussed here are foundational to experimental design. However, we don’t want to give the impression that this is all there is that you need to know. Instead, what is discussed here serves as a guide concerning other topics that need to be investigated.

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