Final answer:
Larger sample sizes tend to provide more reliable results in outcomes research, though smaller samples may be used in specific contexts such as rare conditions. Convenience samples can introduce bias and affect generalizability. Surveys should be carefully designed to avoid leading questions and selection bias.
Step-by-step explanation:
When creating samples for outcomes research, it is generally accepted that larger samples are better as they may provide more reliable results. Although sometimes smaller samples are necessary and can still offer valuable insights, especially in situations where it is difficult or impossible to collect large amounts of data, such as in rare medical conditions or in case studies like crash testing cars. However, using convenience samples, such as students enrolled in Introduction to Psychology courses, can have major implications. These samples are not truly random, which can affect the generalizability of the study's findings. As a result, the research outcomes may not accurately reflect the broader population.
In the context of healthcare research, when studies involve pain management and nursing care, like the use of a software in conjunction with an exercise program, it is crucial to ensure that the samples are not only large enough but also appropriately randomized to avoid skewed results that could compromise the validity of the study. For instance, a sample size of 1,000 may be a reliable measure for a population of 5,000, depending upon the variability and the desired confidence level.
Surveys and sampling methods need to be carefully designed to minimize bias. For example, surveys with leading questions, such as those implying a preference for one brand over another, can influence the respondents and therefore are not considered fair. Similarly, data collection methods, such as mailing surveys and selecting respondents based on return rates, may favor certain groups and are not recommended.