Invasion Of Privacy: The Latest Statistics

An invasion is a military offensive in which large numbers of combatants of one geopolitical entity aggressively enter territory owned by another such entity, generally with the objective of either conquering; liberating or re-establishing control or authority over a territory; forcing the partition of a country; altering the established government or gaining concessions from said government; or a combination thereof. An invasion can be the cause of a war, be a part of a larger strategy to end a war, or it can constitute an entire war in itself. Due to the large scale of the operations associated with invasions, they are usually strategic in planning and execution.Privacy (UK: , US: ) is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively. When something is private to a person, it usually means that something is inherently special or sensitive to them. The domain of privacy partially overlaps with security, which can include the concepts of appropriate use and protection of information. Privacy may also take the form of bodily integrity. The right not to be subjected to unsanctioned invasions of privacy by the government, corporations, or individuals is part of many countries' privacy laws, and in some cases, constitutions. In the field of business, a person may volunteer personal details, including for advertising, in order to receive some kinds of benefit. Public figures may be subject to rules on the public interest. Personal information which is voluntarily shared but subsequently stolen or misused can lead to identity theft. The concept of universal individual privacy is a modern concept primarily associated with Western culture, particularly British and North American, and remained virtually unknown in some cultures until recent times. Most cultures, however, recognize the ability of individuals to withhold certain parts of their personal information from wider society, such as closing the door to one's home.Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena. A standard statistical procedure involves the collection of data leading to test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual relationship between populations is missed giving a "false negative"). Multiple problems have come to be associated with this framework, ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis. Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also occur. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems.