Final answer:
Sources of error in Marta's experiment could include bias, where the sample does not accurately represent the population due to non-random selection, and chance error, which arises from a small sample size that does not capture the population's diversity.
Step-by-step explanation:
Potential sources of error in Marta's experiment could stem from both chance error and bias. Bias is introduced when a sample is not selected randomly or is skewed towards certain characteristics, leading to a systematic error in favor of or against a particular outcome. For instance, the inclusion criteria for subjects in the study might disproportionately represent a particular demographic that does not reflect the diversity of the entire population. Bias affects the validity of the conclusions by providing results that are not generalizable.
Chance error occurs when the sample size is too small, making it likely unrepresentative of the overall population. A small sample size increases the probability that the sample's characteristics occur by chance rather than reflecting the true nature of the population. This can be mitigated by increasing the sample size.
To minimize these errors, Marta can ensure a truly random and sufficiently large sample is selected. She should also remain vigilant about maintaining objectivity and avoid any language or procedures in her methodology that might subtly influence the study's outcomes.