Inferential Statistics for Evidence-Based Decision-Making in Non-Profit Organizations
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global Publishing
Article | NEYA Global Journal of Non-Profit Studies
Year: 2025
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-qn-rchfr-mth-07
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About the Article
Inferential statistics extend quantitative analysis beyond description, enabling non-profit organizations to generalize findings from samples, quantify uncertainty, and test hypotheses. This article synthesizes core elements of inferential practice—including populations and samples, sampling distributions, hypothesis testing, confidence intervals, effect sizes, and common procedures such as t-tests, chi-square tests, ANOVA, and regression. Emphasis is placed on assumptions, design choices, and the interpretation of p-values alongside interval estimates and standardized effects. Practical examples illustrate applications in program evaluation, equity assessment, comparative intervention analysis, and predictive planning. The discussion addresses common pitfalls, such as conflating statistical with practical significance, multiple testing inflation, and biased sampling, offering remedies like pre-specification, multiplicity corrections, robust estimators, and transparent reporting. By integrating rigorous inference with contextual judgment, non-profits can convert sample evidence into credible, actionable guidance for policy, planning, and program improvement.
Key Topics
- Inferential statistics
- Hypothesis testing
- Confidence intervals
- Effect size
- Regression and ANOVA
- Non-profit evaluation
Suggested Citation
Kazanskaia, A. N. (2025). Inferential Statistics for Evidence-Based Decision-Making in Non-Profit Organizations. NEYA Global Journal of Non-Profit Studies. Neya Global Publishing. https://doi.org/10.64357/neya-gjnps-qn-rchfr-mth-07
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