Communication: The Latest Statistics

Communication (from Latin communicare, meaning "to share" or "to be in relation with") is "an apparent answer to the painful divisions between self and other, private and public, and inner thought and outer word." As this definition indicates, communication is difficult to define in a consistent manner, because it is commonly used to refer to a wide range of different behaviors (broadly: "the transfer of information"), or to limit what can be included in the category of communication (for example, requiring a "conscious intent" to persuade). John Peters argues the difficulty of defining communication emerges from the fact that communication is both a universal phenomena (because everyone communicates), and a specific discipline of institutional academic study.One possible definition of communication is the act of developing meaning among entities or groups through the use of sufficiently mutually understood signs, symbols, and semiotic conventions. In Claude Shannon's and Warren Weaver's influential model, human communication was imagined to function like a telephone or telegraph. Accordingly, they conceptualized communication as involving discrete steps: The formation of communicative motivation or reason. Message composition (further internal or technical elaboration on what exactly to express). Message encoding (for example, into digital data, written text, speech, pictures, gestures and so on). Transmission of the encoded message as a sequence of signals using a specific channel or medium. Noise sources such as natural forces and in some cases human activity (both intentional and accidental) begin influencing the quality of signals propagating from the sender to one or more receivers. Reception of signals and reassembling of the encoded message from a sequence of received signals. Decoding of the reassembled encoded message. Interpretation and making sense of the presumed original message.These elements are now understood to be substantially overlapping and recursive activities rather than steps in a sequence. For example, communicative actions can commence before a communicator formulates a conscious attempt to do so, as in the case of phatics; likewise, communicators modify their intentions and formulations of a message in response to real-time feedback (e.g., a change in facial expression). Practices of decoding and interpretation are culturally enacted, not just by individuals (genre conventions, for instance, trigger anticipatory expectations for how a message is to be received), and receivers of any message operationalize their own frames of reference in interpretation.The scientific study of communication can be divided into: Information theory which studies the quantification, storage, and communication of information in general; Communication studies which concerns human communication; Biosemiotics which examines communication in and between living organisms in general. Biocommunication which exemplifies sign-mediated interactions in and between organisms of all domains of life, including viruses.The channel of communication can be visual, auditory, tactile/haptic (e.g. Braille or other physical means), olfactory, electromagnetic, or biochemical. Human communication is unique for its extensive use of abstract language. Development of civilization has been closely linked with progress in telecommunication.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.