Randomize the order (DOE best practice)

[Mithrandil]

It would be useful to add a randomization order option.

Randomization is a crucial element in experimental design for several reasons, especially when dealing with a series of tests that may have some form of temporal sequence, such as starting at low speed and increasing it over time. Here's why randomizing the order of tests is important:

Importance of Randomization

1. Control for Time-Dependent Variables: Over time, variables that you aren't controlling for could change. For example, a machine could heat up, the external temperature could fluctuate, or the material could wear down. Randomizing the order of tests helps to distribute the impact of these time-dependent variables across all levels of your experimental factors.

2. Eliminate Order Effects: In psychological or human-subject experiments, the order in which stimuli are presented can affect the response. For example, subjects may get tired or become more skilled over time.

3. Independence of Observations: Randomization helps ensure that each data point is independent of the others, which is an underlying assumption in many statistical analyses.

4. Mitigate Bias: Systematic bias could be introduced when tests are conducted in a specific order. For example, operators might become more skilled at the task over time, which would confound your results.

5. Improved Generalizability: Randomizing the sequence makes it more likely that your findings will generalize to a broader context or population, because you are less likely to have spurious results due to a confounding variable.

Consequences of Not Randomizing

1. Confounding Variables: You risk introducing a confounding variable that correlates with the variable you are actually interested in, which can lead to incorrect conclusions.

2. Reduced Validity: The validity of the experiment is compromised if there are systematic errors or biases.

3. Statistical Anomalies: Many statistical tests assume that observations are independent and identically distributed. Without randomization, you might violate these assumptions, making your statistical tests invalid or less powerful.

4. Limited Applicability: Your results would be less generalizable, as they would only be directly applicable to the specific set of conditions under which you performed the experiment.

5. Causality Misinterpretation: If the order of tests is not randomized, you might mistakenly attribute the effect to the variable you're studying, when it is actually due to a time-dependent variable or some form of bias.

In summary, the absence of randomization can seriously compromise the quality and reliability of your experiment. Therefore, it's often crucial to randomize the order of tests to ensure that the conclusions drawn are both valid and reliable.