Accounting, Studies, Experiments, Distribution, and Intervals
Fund Accounting
Fund accounting is a method of accounting used by a nonprofit organization that illustrates accountability, rather than profitability. In a business, you want to know how much was spent, how much was earned, and how much was left over. With a nonprofit, you want to know these things, but you also want to make sure that the money you have, receive, and spend is allocated for the proper purpose. Fund accounting is very detailed and can get confusing, but ultimately it is the most accurate method of accounting. By utilizing fund accounting, you can maintain accurate financial records for your organization and all of its directives, thus empowering you to generate powerful financial statements and make key decisions.
Cross-Sectional vs. Longitudinal Studies
Cross-sectional studies make comparisons at a single point in time, whereas longitudinal studies make comparisons over time. The research question will determine which approach is best. Both the cross-sectional and the longitudinal studies are observational studies. This means that researchers record information about their subjects without manipulating the study environment.
Natural Experiments
A natural experiment is an empirical study in which individuals (or clusters of individuals) exposed to the experimental and control conditions are determined by nature or by other factors outside the control of the investigators, but the process governing the exposures arguably resembles random assignment. Thus, natural experiments are observational studies and are not controlled in the traditional sense of a randomized experiment. Natural experiments are most useful when there has been a clearly defined exposure involving a well-defined subpopulation (and the absence of exposure in a similar subpopulation) such that changes in outcomes may be plausibly attributed to the exposure. In this sense, the difference between a natural experiment and a non-experimental observational study is that the former includes a comparison of conditions that pave the way for causal inference, but the latter does not.
Normal Distribution
In probability theory, the normal distribution is a very common continuous probability distribution. Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not known.
The normal distribution is useful because of the central limit theorem. In its most general form, under some conditions (which include finite variance), it states that averages of random variables independently drawn from independent distributions converge in distribution to the normal, that is, become normally distributed when the number of random variables is sufficiently large. Physical quantities that are expected to be the sum of many independent processes (such as measurement errors) often have distributions that are nearly normal. Moreover, many results and methods (such as propagation of uncertainty and least squares parameter fitting) can be derived analytically in explicit form when the relevant variables are normally distributed.
Confidence Intervals
In statistics, a confidence interval (CI) is a type of interval estimate (of a population parameter) that is computed from the observed data. The confidence level is the frequency (i.e., the proportion) of confidence intervals that contain the true value of their corresponding parameter. In other words, if confidence intervals are constructed using a given confidence level in an infinite number of independent experiments, the proportion of those intervals that contain the true value of the parameter will match the confidence level.