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Confirmation bias

Confirmation bias
Tendency of people to favor information that confirms their beliefs or values Confirmation bias, also known as myside bias, is the tendency to search for, interpret, favor, and recall information in a way that confirms or supports one's prior beliefs or values.[1] People display this bias when they select information that supports their views, ignoring contrary information, or when they interpret ambiguous evidence as supporting their existing attitudes. The effect is strongest for desired outcomes, for emotionally charged issues, and for deeply entrenched beliefs. Confirmation bias is a broad construct covering a number of explanations. A series of psychological experiments in the 1960s suggested that people are biased toward confirming their existing beliefs. Flawed decisions due to confirmation bias have been found in political, organizational, financial and scientific contexts. Definition and context[edit] Confirmation bias (or confirmatory bias) has also been termed myside bias. Related:  Science, Environment

Nikon D7000 Review by Thom Hogan Time for the rubber to meet the road (okay, the photons to meet the electronics). Since so many things have changed internally from the D90 there must be some differences, right? You bet your sweet bippy there are. Battery Life New battery, new performance figures. Fortunately, good performance figures. The one drawback to the new battery system is that it isn't exactly a fast charging system. Writing to Card I'm still trying to get a full handle on card performance in the D7000. Raw shooters will not be terribly happy with the buffer size. Autofocus System Surprise, surprise. Mirror up, well, that's another story. Metering System Another surprise. I've noticed a bit of chatter on the net about "overexposure." The corollary is that if you pop up the flash for some fill, the D7000 seems to get that exposure just a little more on target than previous consumer cameras. However, all isn't perfect. White balance is decent to good, especially in mixed lighting. So let me say this: chillax.

Selection bias Selection bias is a statistical bias in which there is an error in choosing the individuals or groups to take part in a scientific study.[1] It is sometimes referred to as the selection effect. The phrase "selection bias" most often refers to the distortion of a statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may not be accurate. Types[edit] There are many types of possible selection bias, including: Sampling bias[edit] A distinction of sampling bias (albeit not a universally accepted one) is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand. Time interval[edit] Exposure[edit] Data[edit] Studies[edit] Attrition[edit] Observer selection[edit] Avoidance[edit] Related issues[edit] See also[edit]

Synchronicity Synchronicity is the occurrence of two or more events that appear to be meaningfully related but not causally related. Synchronicity holds that such events are "meaningful coincidences". The concept of synchronicity was first defined by Carl Jung, a Swiss psychiatrist, in the 1920s.[1] During his career, Jung furnished several slightly different definitions of it.[2] Jung variously defined synchronicity as an "acausal connecting (togetherness) principle," "meaningful coincidence," and "acausal parallelism." He introduced the concept as early as the 1920s but gave a full statement of it only in 1951 in an Eranos lecture.[3] In 1952, he published a paper "Synchronizität als ein Prinzip akausaler Zusammenhänge" (Synchronicity – An Acausal Connecting Principle)[4] in a volume which also contained a related study by the physicist and Nobel laureate Wolfgang Pauli.[5] In his book Synchronicity: An Acausal Connecting Principle, Jung wrote:[6] Description[edit] Examples[edit] Criticisms[edit]

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Illusory correlation History[edit] "Illusory correlation" was originally coined by Chapman and Chapman (1967) to describe people's tendencies to overestimate relationships between two groups when distinctive and unusual information is presented.[5][6] The concept was used to question claims about objective knowledge in clinical psychology through the Chapmans' refutation of many clinicians' widely used Wheeler signs for homosexuality in Rorschach tests.[7] Example[edit] David Hamilton and Robert Gifford (1976) conducted a series of experiments that demonstrated how stereotypic beliefs regarding minorities could derive from illusory correlation processes.[8] To test their hypothesis, Hamilton and Gifford had research participants read a series of sentences describing either desirable or undesirable behaviors, which were attributed to either Group A or Group B.[5] Abstract groups were used so that no previously established stereotypes would influence results. Theories[edit] General theory[edit] Age[edit]

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Survivorship bias Survivorship bias is the logical error of concentrating on the people or things that "survived" some process and inadvertently overlooking those that did not because of their lack of visibility. This can lead to false conclusions in several different ways. The survivors may literally be people, as in a medical study, or could be companies or research subjects or applicants for a job, or anything that must make it past some selection process to be considered further. Survivorship bias can lead to overly optimistic beliefs because failures are ignored, such as when companies that no longer exist are excluded from analyses of financial performance. Survivorship bias is a type of selection bias. In finance[edit] In finance, survivorship bias is the tendency for failed companies to be excluded from performance studies because they no longer exist. For example, a mutual fund company's selection of funds today will include only those that are successful now. As a general experimental flaw[edit]