Key DoE Terms
Your Cheat Sheet for talking DoE
Factors, factor levels, response variable, … there are some terms that you will encounter over and over in DoE. The table below contains the most important ones together with a short definition and an example. Once you familiarize yourself with those terms, you can read the following chapters without getting lost, and we can continue to the next chapter to learn how we design trustworthy experiments using randomization, replication, and blocking.
Essential DoE Terms
| Term | Definition | Example |
|---|---|---|
| Factor | The variables you want to investigate in your experiment. | The effect of temperature on the yield of a chemical reaction. Temperature is a factor. You can include more like pressure, concentration, …, as many as you want. |
| Disturbance Variable | The variables that you do not want to investigate but still might influence your experiment. | The impact of the batch of the raw material. |
| Factor Range | The specified limits or boundaries within which the factors are to be evaluated. | The material decomposes above 250 °C, and the reaction needs at least 120 °C to start. Therefore, a meaningful temperature range is set from 120 °C to 250 °C. |
| Factor Level | The actual values you are going to test within the factor range. | Within the 120 °C to 250 °C range, you can test many temperatures, but you usually choose the extremes (120 °C and 250 °C) and sometimes one in the middle (185 °C). Those are the factor levels. |
| Response Variable | The outcome that is measured. | The yield is your response variable. Again, you can have more than one. |
| Replication | Repeating the same experiment multiple times to account for experimental error and uncontrollable disturbance variables. | You determine how the yield of a chemical reaction varies with temperature. Then, you repeat the test at 150 °C three times. This will give you an idea of the variability in your measurements. |
| Randomization | Conducting experiments in a random order to avoid bias. | Instead of testing temperatures in a sequential order (e.g., 100 °C, 150 °C, 200 °C), you can test them in random order (e.g., 150 °C, 200 °C, 100 °C). |
| Blocking | Blocking in experimental design involves organizing experiments in groups (blocks) to minimize errors caused by known disturbance variables that cannot or are not controlled. | You have two different batches of a material and while you not really interested in the difference of these two batches they might influence your experiment. |
| Variance | Measure of the spread of data points around the mean. | Variance tells you how consistent your measurements are. If the variance is small, the three yields at 200 °C are close together—like 95%, 95.5%, and 96%. If the variance is large, the values are more spread out—like 85%, 92%, and 98%. |
| Residuals | Difference between observed and predicted values in a model. | Residuals tell you how far off your model’s prediction is from the actual result. If your model predicts a yield of 95% at 200 °C but you measure 96%, the residual is +1%. Small residuals mean your model fits the data well—large ones mean it’s missing something. |
| ANOVA | Analysis of Variance; tests whether differences between group means are statistically significant. | If you measure yields at 120 °C and 250 °C; ANOVA checks if the changes in yield that you observe are real or just happened by chance. |
| Significant | Means the result is unlikely to be due to chance and likely reflects a real effect. | If the yield at 250 °C is significantly higher than at 120 °C, it suggests temperature truly affects the reaction and the variation was not random. |
| P-value | Output of ANOVA. Indicates how likely it is that your observations are by chance. | If you measure yields at 120 °C and 250 °C, and ANOVA outputs a p-value of 0.04, the likelihood that the observed difference in yield was due to chance is only 4%. |