A Scientist Testifying Before A Congressional Hearing

1 A Scientist Testifying Before A Congressional Hearing On The Effect

Identify the population, statistical population, sample and statistical sample. A scientist testified before a congressional hearing about the effects of estrogenic pesticides, discussing a report involving 14,847 men from various countries and years, showing a 50% decline in male semen concentration over the past 50 years. Additionally, a statistics professor aims to determine the average GPA of all students at UIUC, using his class as a sample, collecting their GPAs to estimate this average.

Paper For Above instruction

The concepts of population, statistical population, sample, and statistical sample are foundational in statistical analysis, crucial for understanding how data represent larger groups and how inferences are drawn.

Analysis of the Scientist’s Testimony

The population, in this case, refers to all men worldwide, as the report discusses global studies. This is the entire group that the findings intend to represent. The statistical population aligns closely with the population, encompassing all men of the world in the context of the study, although if the scope was limited to a certain age group or region, this would redefine the statistical population accordingly.

The sample comprises the 14,847 men involved in the various studies. A sample is a subset of a larger population selected for analysis, which enables researchers to make inferences about the entire population. In this scenario, the sample is drawn from the population of all men worldwide, based on the studies conducted in different countries and years.

The statistical sample specifically refers to the data collected from those 14,847 men included in the report. They are the actual data points used to analyze trends and establish conclusions regarding semen concentration declines. These individuals form the data collection unit from which inferences about the larger population are made.

Analysis of the GPA Study at UIUC

The population here consists of every student enrolled at the University of Illinois at Urbana-Champaign (UIUC). The statistical population similarly includes all UIUC students, as the goal is to understand the overall average GPA of this group.

The sample in this context is the subset of UIUC students who are enrolled in the statistics class taught by the professor. The professor uses this group of students to gather GPA data, believing it to be representative enough to estimate the average GPA for all UIUC students.

The statistical sample is the actual collection of GPA data from the students in the class, which serves as the data unit for calculating the estimated average GPA. This sample provides a manageable and practical way to infer the broader academic achievement levels across the entire student body.

Implications and Importance

Understanding these distinctions helps in designing studies and interpreting results correctly. The population is the entire group of interest, while the sample is a manageable subset. Proper sampling ensures that inferences made are valid and representative, facilitating evidence-based decision making and policy development in public health and education sectors.

Conclusion

Both examples demonstrate how defining the population, statistical population, sample, and statistical sample is essential for effective research. Proper identification ensures clarity in methodology, aids in eliminating biases, and enhances the reliability of conclusions drawn from data analysis.

References

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  • Fowler, F. J. (2013). Survey Research Methods (5th ed.). Sage Publications.
  • Levy, P. S., & Lemeshow, S. (2013). Sampling of Populations: Methods and Applications. Wiley.
  • Lohr, S. (2009). Sampling: Design and Analysis. Brooks/Cole.
  • Neuman, W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches. Pearson.
  • Hogg, R. V., et al. (2018). Introduction to Mathematical Statistics. Pearson.
  • Johnson, R. A., & Wichern, D. W. (2018). Applied Multivariate Statistical Analysis. Pearson.
  • Pestana, S., & Gage, L. (2010). Statistics for Business and Economics. Cengage Learning.
  • Vogt, W. P. (2011). Dictionary of Statistics & Methodology: A Nontechnical Guide for the Social Sciences. Sage Publications.
  • Rubin, D. B. (2004). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons.

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