1. Representativeness:
* Accuracy: The sample should accurately reflect the characteristics of the population of interest. This means the proportions of different subgroups (e.g., age, gender, ethnicity) within the sample should be similar to their proportions in the population. Bias in sampling can severely compromise representativeness.
* Coverage: The sample should include members from all relevant subgroups within the population. Missing entire segments of the population leads to biased results and limits generalizability.
2. Size:
* Sufficiently Large: The sample needs to be large enough to detect meaningful differences or relationships. A smaller sample might be acceptable if the effect size is large and variability is low, but generally, larger samples are preferred. Power analysis can help determine the appropriate sample size.
* Not too Large: While larger is generally better, excessively large samples can be inefficient and costly without providing proportionally more benefit.
3. Randomness (for probability sampling):
* Random Selection: Ideally, each member of the population has an equal chance of being selected. This reduces bias and allows for generalization to the population. Different types of random sampling exist (simple random, stratified random, cluster random, etc.).
* Independence: The selection of one individual should not influence the selection of another.
4. Accessibility:
* Feasibility: The sample should be practically accessible to the researcher. This involves considering factors like cost, time, and logistics.
* Cooperation: Members of the selected sample should be willing to participate in the study. Low response rates can introduce bias.
5. Appropriateness:
* Relevance: The sample should be appropriate for the research question being investigated. For example, if studying the effects of a new drug, the sample should include individuals who would be eligible for the treatment.
* Homogeneity vs. Heterogeneity: The level of homogeneity or heterogeneity needed depends on the research question. Studying a very specific phenomenon might require a homogeneous sample, while investigating broader trends might need a more heterogeneous one.
In summary, a good sample is representative, sufficiently large, randomly selected (for probability sampling), accessible, and appropriate for the research question. Striking a balance between these characteristics is crucial for ensuring the validity and reliability of research findings.