Statistical Inference

Casella, G., & Berger, R. L. (2002). Statistical Inference. Duxbury Press.

Type

Book

Intellectual base

Review or monograph

Year

2002

ISBN

9780534243128

Link

https://www.cengage.com/c/statistical-inference-2e-casella-berger/9780534243128/

Cited from

mathematics, statistics_experimental_design

Cited by

10 citations across 10 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Bias
  • Calling a single surprising result "biased" confuses an instance with a tendency.

    This sourceStandard graduate text on point estimation: defines bias as a property of an estimator's expectation (visible only across repeated application, never in one draw), and develops the downward-biased sample variance, the n−1 (Bessel) correction, and the finite-sample bias of maximum-likelihood estimators.

• Central Limit Theorem
  • Statistics and inference — confidence intervals, hypothesis tests, and standard errors rest on the CLT-driven asymptotic normality of estimators, even when the underlying data are non-normal.

    This sourceGrounds confidence intervals, hypothesis tests, and standard errors on the CLT-driven asymptotic normality of estimators and the 1/√n shrinkage of the standard error.

• Correlation
  • The defining commitment is statistical association as a self-standing fact: knowing the value of one variable updates the probability distribution over the other, yet the association is silent about which (if either) drives which, leaving open common-cause, reverse-cause, mediated, or coincidental explanations.

    This sourceStandard graduate text; Chapter 4 (Multiple Random Variables) develops joint and marginal distributions, conditional distributions and independence, and covariance and correlation — including that the joint distribution of dependent variables does not factor into the product of its marginals, and that zero (linear) correlation does not imply independence.

• Statistical Independence
  • Probability and statistics: the factorization is the foundational license for IID sampling, naive Bayes, bootstrap resampling, and product-form likelihoods.

    This sourceEstablishes IID sampling, product-form likelihoods, and the role of independence as a premise across statistical methods.

• Statistical Inference
  • The structural insight is robust, as Casella and Berger (2002) emphasize in their canonical treatment: whether estimating a treatment effect in a clinical trial, validating a machine-learning model on unseen data, inferring a disease prevalence from a sample survey, or detecting contamination in a manufacturing process, the template remains: specify a model, estimate parameters with uncertainty, and reason about what the data support.

    This sourceStandard graduate text on point estimation: defines bias as a property of an estimator's expectation (visible only across repeated application, never in one draw), and develops the downward-biased sample variance, the n−1 (Bessel) correction, and the finite-sample bias of maximum-likelihood estimators.

    Supported in partVerified against the work's full text

    Casella & Berger provide the general statistical inference template; the claim's four specific application domains are not attributed to them.

    “This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts.”

    Read by an automated reader; not a human review. How support was checked

Domain-specific

• Continuous Uniform Distribution
  • Sample minimum and maximum then carry direct endpoint information, and familiar likelihood regularity shortcuts may fail.
• Method of Moments
  • Modern texts treat it as a general point-estimation principle and analyze consistency through convergence of sample moments plus identifiability.
• Probability integral transform
  • The entry records a descriptive analytical identity; practical use requires the governing domain's evidence, standards, and safety obligations.
• Random Variable
  • The random variable is the foundational technical object of probability theory: estimators, test statistics, p-values, likelihood ratios, and posterior distributions are all random variables, as are portfolio returns, queue lengths, service times, and quantum observables (in the classical sense of their measurement outcomes)

    This sourceEstablishes the statistical half of the list -- estimators, test statistics, likelihood ratios and p-values as random variables with sampling distributions; the finance, queueing and quantum instances are elsewhere.

    Supported in partVerified against the source

    Read by an automated reader; not a human review. How support was checked

• Statistic
  • Casella and Berger emphasize the parameter-free sample-function requirement because an expression containing an unknown \(\theta\) cannot be calculated from the data alone and is not a statistic in that model.

Verification

Does it exist? Confirmed. This work's ISBN resolves to a registered record, which fixes its identity. That is all it fixes.

Does it back the claim? Read against the text for 2 of 10 citations: 2 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.

Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.

See how references were verified.

Links previously used in the corpus

Before the registry existed this work was also linked 2 other ways.

• https://www.cengage.com/c/statistical-inference-2e-casella/9780534243128/ ×1
• https://www.routledge.com/Statistical-Inference/Casella-Berger/p/book/9781032593036 ×1

Registry ID ref:16fe0b890b1c · see in the full table