Final answer:
Representativeness of a sample is determined by the sample's recruitment methods, size, and randomness. A valid sample can be achieved with correct sampling techniques; ethical considerations are also crucial in the research process. Biased sampling methods, like convenience sampling or systematic sampling without randomness, may not yield representative results.
Step-by-step explanation:
Understanding Sample Representativeness and Research Methods
When considering whether a sample is representative of a larger population, many factors come into play. For instance, a sample of patients who are involved in an exercise program may yield results that cannot be generalized to a wider population that does not engage in the same behaviors. Similarly, recruiting patients from a specific hospital or a community sample may introduce biases if the subjects do not reflect the diversity of the broader population they are meant to represent.
In judging the representativeness of a sample, one needs to consider the recruitment methods, such as whether it uses a simple random sample or involves systematic methods. A sample size of 1,000 for a population of 5,000 or a sample of 500 for a population of 2,500 can be appropriate if the sampling is done correctly and is random. However, the validity can be compromised if the sampling is non-random or biased, as with convenience sampling, or if the survey questions are leading as in the case of a question that describes one brand as 'delicious'. Furthermore, in this field, ethical considerations such as voluntary participation, fairness in the selection of research subjects, confidentiality, risk to participants, and the potential societal benefits of the study are paramount.
For instance, in a situation where a medical researcher interviews every third cancer patient, this would constitute a systematic sampling method, which is different from simple random sampling and may not be as reflective of the general cancer patient population. On the other hand, a high school counselor using a random number generator to select students could achieve a random sample, assuming the total population was well-represented by the list of students. Ultimately, both samples from the initial scenario are likely to be biased and, thus, not perfectly representative of the general population of part-time students.