Srivastava views statistical inference through two distinct lenses: Theory of Estimation Testing of Hypotheses
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While estimation seeks to approximate a specific value, evaluates claims about a population. Srivastava’s work guides students through the rigorous mathematical proofs required to determine if an observed effect is statistically significant or merely the result of random chance. This involves balancing Type I errors (false positives) and Type II errors (false negatives) to ensure the reliability of scientific conclusions. 3. Classical vs. Bayesian Perspectives This involves balancing Type I errors (false positives)
The sequel to the first book, this volume introduces estimation problems following the foundations set by Sir R.A. Fisher in 1922.
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: Co-authored with Abdul Hamid Khan and Namita Srivastava, this text focuses on point and interval estimation using both classical and Bayesian approaches. Statistical Inference: Testing of Hypotheses