Examples of fundamental attribution error in the following topics:
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- A few common such biases include the fundamental attribution error, the self-serving bias, the actor-observer bias, and the just-world hypothesis.
- This perspective is called the fundamental attribution error and may result from our attempt to simplify the processing of complex information.
- The fundamental attribution error is so powerful that people often overlook even obvious situational influences on behavior.
- People from individualist cultures are more inclined to make the fundamental attribution error and demonstrate self-serving bias than people from collectivist cultures.
- The fundamental attribution error explains why when someone cuts us off we assume he or she is bad-natured, but when we cut someone off we believe it is because the situation required it.
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- The field is also concerned with common cognitive biases—such as the fundamental attribution error, the actor-observer bias, the self-serving bias, and the just-world hypothesis—that influence our behavior and our perceptions of events.
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- Random, or chance, errors are errors that are a combination of results both higher and lower than the desired measurement.
- While conducting measurements in experiments, there are generally two different types of errors: random (or chance) errors and systematic (or biased) errors.
- It may even be that whatever we are trying to measure is changing in time or is fundamentally probabilistic.
- In this case, there is more systematic error than random error.
- In this case, there is more random error than systematic error.
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- Systematic errors are biases in measurement which lead to a situation wherein the mean of many separate measurements differs significantly from the actual value of the measured attribute.
- If it is within the margin of error for the random errors, then it is most likely that the systematic errors are smaller than the random errors.
- Instrumental Errors: Instrumental errors are attributed to imperfections in the tools with which the analyst works.
- In this case, there is more random error than systematic error.
- In this case, there is more systematic error than random error.
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- This can include errors as well as accounting mismanagement, which involves distorting the raw data used to derive financial ratios.
- While the weak form of this hypothesis argues that there can be a long run benefit to information derived from fundamental analysis, stronger forms argue that fundamental analysis like ratio analysis will not allow for greater financial returns.
- In another view on stock markets, technical analysts argue that sentiment is as much if not more of a driver of stock prices than is the fundamental data on a company like its financials.
- Behavioral economists attribute the imperfections in financial markets to a combination of cognitive biases such as overconfidence, overreaction, representative bias, information bias, and various other predictable human errors in reasoning and information processing.
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- Chance error and bias are two different forms of error associated with sampling.
- In statistics, a sampling error is the error caused by observing a sample instead of the whole population.
- In sampling, there are two main types of error: systematic errors (or biases) and random errors (or chance errors).
- Random error always exists.
- If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling.
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- In order to prepare the financial statements, it is important to adhere to certain fundamental accounting concepts.
- In order to prepare the financial statements, it is important to adhere to certain fundamental accounting concepts.
- Money Measurement, accounts only deal with items to which monetary values can be attributed.
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- This question relates an attribute (gender) to a measure of the actor's position in a network (between-ness centrality).
- We might even be interested in the relationship between two individual attributes among a set of actors who are connected in a network.
- These variables may be either non-relational attributes (like gender), or variables that describe some aspect of an individual's relational position (like between-ness).
- Instead of applying the normal formulas (i.e. those built into statistical software packages and discussed in most basic statistics texts), we need to use other methods to get more correct estimates of the reliability and stability of estimates (i.e. standard errors).
- The "boot-strapping" approach (estimating the variation of estimates of the parameter of interest from large numbers of random sub-samples of actors) can be applied in some cases; in other cases, the idea of random permutation can be applied to generate correct standard errors.
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- But, rather than describing distributions of attributes of actors (or "variables"), we are concerned with describing the distributions of relations among actors.
- Most of the descriptive statistical tools are the same for attribute analysis and for relational analysis -- but the subject matter is quite different!
- In network analysis, we focus on relations, not attributes.
- The standard formulas for computing standard errors and inferential tests on attributes generally assume independent observations.
- Instead, alternative numerical approaches to estimating standard errors for network statistics are used.
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- Christian Fundamentalism, also known as Fundamentalist Christianity, or Fundamentalism, arose out of
British and American Protestantism in the late 19th and early 20th centuries
among Evangelical Christians.
- The founders of
Fundamentalist Christianity reacted against liberal theology and militantly
asserted that the inerrancy, meaning without error or fault, of the Bible was essential
for true Christianity and was being violated by modernists.
- Fundamentalism
has roots in British and American theology of the 19th century.
- They
also addressed what was considered the falsity of theological systems such as Christian
Science, "Millennial Dawnism", and Mormonism, as well as the errors
of "Romanism".
- Analyze the origins of Christian Fundamentalism in late 19th- and early 20th-century America