পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Confidence Interval× | প্রভাবের আকার× | |
|---|---|---|
| ক্ষেত্র | গবেষণা পরিসংখ্যান | গবেষণা পরিসংখ্যান |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1937 | 1988 |
| প্রবর্তক≠ | Jerzy Neyman | Jacob Cohen |
| ধরন | Concept | Concept |
| মৌলিক উৎস≠ | Neyman, J. (1937). Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability. Philosophical Transactions of the Royal Society, 236, 333–380. DOI ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5 |
| অপর নাম | CI, 95% CI, credible interval, interval estimate | ES, Cohen's d, standardized effect, practical significance |
| সম্পর্কিত | 4 | 4 |
| সারসংক্ষেপ≠ | A confidence interval (CI) is a range of values, calculated from sample data, that likely contains the true population parameter. Introduced by Jerzy Neyman in 1937, it provides an interval estimate rather than a single point estimate, incorporating both the observed value and the uncertainty around it. The standard 95% confidence interval is a robust, intuitive alternative to p-values for communicating research results. | Effect size quantifies the magnitude of a research finding independent of sample size. While a p-value tells you whether a result is statistically significant, an effect size tells you how big the result is. Jacob Cohen formalized effect size measurement in behavioral sciences (1988), establishing standard benchmarks (small = 0.2, medium = 0.5, large = 0.8 for Cohen's d). Effect sizes are essential for meta-analysis, power analysis, and communicating the practical importance of research findings. |
| ScholarGateডেটাসেট ↗ |
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