Final answer:
The explanatory variable in the scenario is the amount of rain in the growing season, and the response variable is the yield of corn per acre. Controlling for other factors and preventing lurking variables from affecting the conclusion is crucial in such experiments to help establish a causal relationship.
Step-by-step explanation:
In the scenario provided, we are examining how the yield of corn in bushels per acre is influenced by the amount of rain in the growing season. The explanatory variable is the amount of rain, as it is the variable that is presumed to affect the other variable. The response variable is the yield of corn per acre since it is the variable that we expect to change in response to the amount of rain. When designing an experiment, scientists must control other factors that might affect the dependent variable, ensuring that the independent variable is the only one affecting the outcome. For instance, in an experiment involving plants, factors such as sunlight and soil quality need to be controlled. An example of a control in an experiment is when one row of plants, like corn, is not given any fertilizer while all other conditions remain the same, allowing a scientist to compare the growth against the fertilized row.
When conducting an experiment to study the relationship between variables, it is crucial to account for lurking variables and to design the experiment with only one difference between groups, which are the planned treatments. Such precision in experimental design is achieved by random assignment of experimental units to treatment groups, which helps in proving causation rather than mere correlation.