Final answer:
When systematic missing data is identified, the impact on research results must be analyzed. It is important not to omit relevant data as it can induce sample bias. Examination of outliers, variability, and criteria for measurement acceptance are vital for reliable statistical analysis.
Step-by-step explanation:
When data is missing in a systematic way, it's crucial to analyze the impact of such missing data on the research results. If relevant data is intentionally omitted, as in the scenario where a researcher excludes information from people who are not home during a study on jobs and child care, it can lead to sample bias. Such a bias occurs because a significant portion of the target population, in this case, working families, may be missing from the study, thereby skewing the results.
Furthermore, the decision to include or exclude data should be based on objective criteria. For instance, when outliers are identified, they need to be critically examined to determine whether they are a result of incorrect data or if they actually contain valuable information about the population. The practice of using null models and thresholds for Type I errors in hypothesis testing has also been scrutinized, with many advocating for the abandonment of these subjective statistical practices.
Different forms of variability, such as measurement, natural, induced, and sampling variability, can explain differences in outcomes. It is important to have a large, well-chosen sample size that is representative of the population to minimize these variabilities and improve the reliability of the sample. Additionally, criteria for the acceptance of measurements or observations may vary depending on whether the results are expected or unexpected. In all cases, statistical data requires critical analysis to ensure its reliability.