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These assertions are relevant to auditors performing a financial statement audit in two ways. First, the objective of a financial statement audit is to obtain sufficient appropriate audit evidence to conclude on whether the financial statements present fairly, in all material respects, the financial position of a company and the results of its ...
Substantive procedures (or substantive tests) are those activities performed by the auditor to detect material misstatement at the assertion level. [1]Management implicitly assert that account balances and disclosures and underlying classes of transactions do not contain any material misstatements: in other words, that they are materially complete, valid and accurate.
A review in USA Today called the book "a gut-wrenching wakeup call". [9] Thomas L. Friedman, in his op-ed column in The New York Times, called the book "insightful", agreeing with Romm's arguments in the book that the proposed "cap and trade" climate bill "is a step in the right direction toward reducing greenhouse gases and expanding our base of clean power technologies". [10]
In statistics, asymptotic theory, or large sample theory, is a framework for assessing properties of estimators and statistical tests. Within this framework, it is often assumed that the sample size n may grow indefinitely; the properties of estimators and tests are then evaluated under the limit of n → ∞. In practice, a limit evaluation is ...
In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets. They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part.
The National Intelligence Council's recommendations described the use of a WEP paradigm (table 2) in combination with an assessment of confidence levels ("high, moderate, low") based on the scope and quality supporting information:
In statistical estimation theory, the coverage probability, or coverage for short, is the probability that a confidence interval or confidence region will include the true value (parameter) of interest. It can be defined as the proportion of instances where the interval surrounds the true value as assessed by long-run frequency.