A block is a categorical variable that explains variation in the response variable that is not caused by the factors. Although each measurement should be taken under consistent experimental conditions (other than the factors that are being varied as part of the experiment), this is not always possible. Use blocks in designed experiments and analysis to minimize bias and variance of the error because of nuisance factors. For example, you want to test the quality of a new printing press. However, press arrangement takes several hours and can only be done four times a day. Because the design of the experiment requires at least eight runs, you need at least two days to test the press. You should explain any differences in conditions between days by using "day" as a blocking variable. To distinguish between any block effect (incidental differences between days) and effects because of the experimental factors (temperature, humidity, and press operator), you must include the block (day) in the designed experiment. You should randomize run order within blocks.