Linganisha mbinu

	Effect size analysis	Upimaji wa t wa sampuli huru
Nyanja	Takwimu	Takwimu
Familia	Hypothesis test	Hypothesis test
Mwaka wa asili	1969 (first edition); 1988 (definitive second edition)	1908
Mwanzilishi	Jacob Cohen	Student (W. S. Gosset)
Aina	Standardized magnitude estimation	Parametric mean comparison
Chanzo asilia	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832	Student (W. S. Gosset) (1908). The probable error of a mean. Biometrika, 6(1), 1–25.
Majina mbadala	effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis	two-sample t-test, unpaired t-test, Student t-test, independent groups t-test
Zinazohusiana	4	4
Muhtasari	Effect size analysis quantifies the practical magnitude of a statistical result independently of sample size. Rather than asking only whether a difference or relationship is statistically significant, it asks how large it is, using standardized indices such as Cohen's d, eta-squared, omega-squared, or Pearson's r that allow direct comparison across studies and populations.	The independent samples t-test is a parametric hypothesis test that determines whether the means of two independent, unrelated groups differ significantly on a continuous outcome variable. Derived from Gosset's 1908 t-distribution, it is one of the most widely used inferential tests in social, behavioral, biomedical, and experimental sciences.