Theories are not merely descriptions of 'what' happens, but explanations of 'why' phenomena occur. Furthermore, a primary goal of scientific theory is to provide predictive power for novel situations, not just to account for existing facts. Since both statements 2 and 3 are inaccurate descriptions of scientific theories, option C is the correct choice.

Collecting a large, representative sample is crucial in research because it provides the statistical power necessary to generalize findings from the sample to the broader population. When a sample is sufficiently large and diverse, researchers can be more confident that the patterns observed in their data accurately reflect the characteristics of the entire group they are studying, thereby increasing the external validity of the research.

Random sampling is the gold standard for ensuring that a sample accurately represents the larger population. By giving every member of the population an equal chance of being selected, researchers minimize selection bias and increase the generalizability of their findings. This method helps ensure that the characteristics of the sample reflect the diversity and distribution of the target population, which is essential for valid scientific inference.

The survey method relies on gathering data from a subset of a population to make generalizations about the larger group. If the sample is not representative of the population, the results will be biased and cannot be accurately generalized. Therefore, representative sampling is a fundamental component of valid survey research design.

A sample is biased when it does not accurately reflect the diversity of the target population. By calling only during work hours, the researcher systematically excludes employed individuals, creating a sample that is not representative of the general public. This selection bias limits the ability to generalize the findings to the entire population.

Source answer preserved: option C (cohort effects). AI attempted to change protected answer data (option_a, option_b, option_c, option_d), so this item is flagged for manual review before study use.

Sample bias occurs when the method of selecting participants leads to a sample that does not accurately reflect the characteristics of the target population. This can compromise the external validity of a study, as findings from a biased sample may not generalize to the entire population, leading to potentially misleading conclusions.

In research methodology, the 'population' refers to the entire group of interest, which in this case is all 1,000 students. A 'sample' is a smaller, representative subset selected from that population to participate in the study, allowing researchers to draw inferences about the larger group.

Representative sampling involves selecting a subset of individuals from a larger population that accurately reflects the population's diverse characteristics. Randomly selecting participants from the larger population is the most effective and unbiased method for ensuring that every member has an equal chance of being chosen, thereby minimizing selection bias.

A major challenge of the survey method is ensuring that the sample of participants is representative of the larger population being studied. If the sample is biased or not diverse enough, the results cannot be accurately generalized to the entire population, which undermines the external validity of the research findings.