For This Assignment You Will Examine Statistical Significanc ✓ Solved

For This Assignment You Will Examine Statistical Significance And Mea

For this Assignment, you will examine statistical significance and meaningfulness based on sample statements. To prepare for this Assignment: Review the Week 5 Scenarios found in this week’s Learning Resources and select two of the four scenarios for this Assignment. For additional support, review the Skill Builder: Evaluating P Values and the Skill Builder: Statistical Power. For this Assignment: Critically evaluate the two scenarios you selected based upon the following points: Critically evaluate the sample size. Critically evaluate the statements for meaningfulness. Critically evaluate the statements for statistical significance. Based on your evaluation, provide an explanation of the implications for social change. Use proper APA format and citations, and referencing.

Sample Paper For Above instruction

Introduction

Understanding the concepts of statistical significance and meaningfulness is crucial in evaluating research findings, especially in social science research. These elements help determine whether observed effects are likely due to chance or represent a real effect, and whether they have practical implications for social change. This paper critically evaluates two selected research scenarios from Week 5 based on their sample size, statistical significance, meaningfulness, and explores the implications of these evaluations for social change.

Scenario 1 Analysis

In the first scenario, a study investigates the impact of a new educational program on student performance. The sample size consists of 150 students from a single school district. The statistical analysis reports a p-value of 0.04, suggesting the results are statistically significant at the alpha level of 0.05. The effect size is moderate, and the researchers claim the findings are meaningful for educational policy change.

Evaluation of Sample Size

The sample size of 150 students appears appropriate for this type of educational research. However, considering the diversity of student populations, a larger sample might have enhanced the representativeness and generalizability of the results. Small samples limit the power of statistical tests and the ability to detect true effects, which may lead to Type II errors if the sample size were smaller.

Evaluation of Meaningfulness

The claim of meaningfulness depends on the effect size and practical significance. A moderate effect size indicates some practical impact, but whether this translates into meaningful social change depends on the context — for example, the extent to which this program improves educational outcomes across diverse settings.

Evaluation of Statistical Significance

The p-value of 0.04 indicates the results are statistically significant, suggesting that the observed differences are unlikely due to chance alone. However, statistical significance does not imply practical significance. The effect size and confidence intervals provide additional insight into the relevance of the findings.

Scenario 2 Analysis

The second scenario involves a health intervention study where 50 participants receive a new therapy. The p-value is 0.09, which is above the standard threshold of 0.05, indicating the results are not statistically significant. The researchers suggest potential benefits of the therapy but advise caution.

Evaluation of Sample Size

The small sample size of 50 limits the statistical power, increasing the risk of Type II errors — failing to detect real effects. A larger sample could potentially yield a significant result if the effect exists.

Evaluation of Meaningfulness

Despite the lack of statistical significance, the observed trend might still carry practical importance, especially if the effect size is substantial. Clinicians might consider these preliminary findings as indicative of a need for further research rather than dismissing the intervention entirely.

Evaluation of Statistical Significance

The p-value of 0.09 suggests that the findings could be due to chance, and caution should be exercised in interpreting these results. Emphasizing effect size and confidence intervals can provide more context about potential benefits.

Implications for Social Change

The evaluation of these scenarios illustrates how sample size, statistical significance, and meaningfulness influence research interpretation. For social change, findings with adequate sample sizes and meaningful effects are more likely to inform policies or interventions. Recognizing the limitations of small samples and p-values is essential for developing evidence-based practices that genuinely benefit society.

In conclusion, careful critical evaluation of research scenarios based on statistical and practical considerations guides responsible application of findings and promotes effective social change initiatives. Ensuring sufficient sample sizes and interpreting the significance and meaningfulness of results helps bridge research and practical social applications, ultimately enhancing societal well-being.

References

  • American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
  • Fletcher, R., & Fletcher, S. (2017). Clinical epidemiology: The essentials (5th ed.). Wolters Kluwer.
  • Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
  • Harlow, L. L. (2014). An introduction to statistical methods and data analysis. Pearson.
  • Levin, K. A. (2006). Study design III: Cross-sectional studies. Evidence-Based Dentistry, 7(1), 24-25.
  • Schunk, D. H. (2012). Learning theories: An educational perspective (6th ed.). Pearson.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
  • Thabane, L., Ma, J., Chu, R., et al. (2013). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 13, 1-10.
  • Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594-604.

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