Final answer:
When we do not run an experiment, problems such as simultaneity, alternative causes, reverse causality, and survivorship bias can arise. These issues affect the validity of the study by obscuring the true cause and effect relationships and could lead to unrepresentative results.
Step-by-step explanation:
When an experiment is not run, several issues can arise that impact the validity of the observations. Issues of simultaneity, alternative causes, reverse causality, and survivorship bias, are all potential problems. For instance, without an experiment, it's difficult to determine if a change in variable X causes a change in variable Y or vice versa (reverse causality), or if an unaccounted variable is influencing both (alternative causes). Additionally, only observing individuals or cases that 'survive' or make it through a selection process could lead to skewed or unrepresentative results (survivorship bias). The question of whether randomization is a problem if we do not run an experiment, it depends on the context. In experimental design, the lack of randomization may lead to sampling bias and significantly affect the study's validity. Randomized samples are crucial to minimize biases and ensure that everyone has the same chance of being included in the study (d. Everyone has the same chance of being part of the study).