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Confidence accounting is a method of accounting whereby some of the figures are expressed not as single point estimates, but rather as probability distributions.Under Confidence Accounting, the end results of audits would be presentations of distributions for major entries in the profit & loss, balance sheet and cashflow statements.
The colored lines are 50% confidence intervals for the mean, μ. At the center of each interval is the sample mean, marked with a diamond. The blue intervals contain the population mean, and the red ones do not. In statistics, a confidence interval (CI) is a tool for estimating a parameter, such as the mean of a population. [1]
For a confidence level, there is a corresponding confidence interval about the mean , that is, the interval [, +] within which values of should fall with probability . Precise values of z γ {\displaystyle z_{\gamma }} are given by the quantile function of the normal distribution (which the 68–95–99.7 rule approximates).
A great advantage of bootstrap is its simplicity. It is a straightforward way to derive estimates of standard errors and confidence intervals for complex estimators of the distribution, such as percentile points, proportions, Odds ratio, and correlation coefficients.
Confidence bands can be constructed around estimates of the empirical distribution function.Simple theory allows the construction of point-wise confidence intervals, but it is also possible to construct a simultaneous confidence band for the cumulative distribution function as a whole by inverting the Kolmogorov-Smirnov test, or by using non-parametric likelihood methods.
The procedure proposed by Dunn [2] can be used to adjust confidence intervals. If one establishes m {\displaystyle m} confidence intervals, and wishes to have an overall confidence level of 1 − α {\displaystyle 1-\alpha } , each individual confidence interval can be adjusted to the level of 1 − α m {\displaystyle 1-{\frac {\alpha }{m}}} .
Nearly 160 accounting execs and partners were asked about why firms were making more auditing errors. The auditors were split on whether a better work-life balance could reduce the number of errors.
A common misconception of confidence intervals is 100γ% of the data set fits within or above/below the bounds, this is referred to as a tolerance interval, which is discussed below. There are multiple methods used to build a confidence interval, the correct choice depends on the data being analyzed.