Examples of bias in the following topics:
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- This section discusses various types of sampling biases including self-selection bias and survivorship bias.
- A common type of sampling bias is to sample too few observations from a segment of the population.
- Gains in stock funds is an area in which survivorship bias often plays a role.
- Therefore, there is a bias toward selecting better-performing funds.
- There is good evidence that this survivorship bias is substantial (Malkiel, 1995).
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- Chance error and bias are two different forms of error associated with sampling.
- In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others.
- Self-selection bias, which is possible whenever the group of people being studied has any form of control over whether to participate.
- Exclusion bias, or exclusion of particular groups from the sample.
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- Failure to use probability sampling may result in bias or systematic errors in the way the sample represents the population.
- Another source of bias is nonresponse, which occurs when a selected individual cannot be contacted or refuses to participate in the survey.
- A third example of bias is called response bias.
- Careful training of pollsters can greatly reduce response bias.
- Finally, another source of bias can come in the wording of questions.
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- Descriptive statistics can be manipulated in many ways that can be misleading, including the changing of scale and statistical bias.
- Bias is another common distortion in the field of descriptive statistics.
- The following are examples of statistical bias.
- Analytical bias arises due to the way that the results are evaluated.
- Exclusion bias arises due to the systematic exclusion of certain individuals from the study
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- As some people do not answer calls from strangers, or may refuse to answer the poll, poll samples are not always representative samples from a population due to what is known as non-response bias.
- In this type of bias, the characteristics of those who agree to be interviewed may be markedly different from those who decline.
- However, if those who do not answer have different opinions, then the results have bias.
- In terms of election polls, studies suggest that bias effects are small, but each polling firm has its own techniques for adjusting weights to minimize selection bias.
- Undercoverage is a highly prevalent source of bias.
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- This has not negated the fact that gender bias exists in higher education.
- O'Connell entitled "Sex Bias in Graduate Admissions: Data from Berkeley. " This study was conducted in the aftermath of a law suit filed against the University, citing admission figures for the fall of 1973, which showed that men applying were more likely than women to be admitted, and the difference was so large that it was unlikely to be due to chance.
- In fact, most departments had a small but statistically significant bias in favor of women.
- Therefore, the admission bias seemed to stem from courses previously taken.
- One of the best real life examples of the presence of confounding variables occurred in a study regarding sex bias in graduate admissions here, at the University of California, Berkeley.
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- Bias is sometimes known as systematic error.
- Bias in a data set occurs when a value is consistently under or overestimated.
- Bias can also arise from forgetting to take into account a correction factor or from instruments that are not properly calibrated.
- Bias leads to a sample mean that is either lower or higher than the true mean .
- An estimate of expected error in the sample mean of variable $A$, sampled at $N$ locations in a parameter space $x$, can be expressed in terms of sample bias coefficient $\rho$ -- defined as the average auto-correlation coefficient over all sample point pairs.
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- While some sampling variation is expected, we would expect the sample proportions to be fairly similar to the population proportions if there is no bias on juries.
- H 0 : The jurors are a random sample, i.e. there is no racial bias in who serves on a jury, and the observed counts reflect natural sampling fluctuation.
- H A : The jurors are not randomly sampled, i.e. there is racial bias in juror selection.
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- It also commonly occurs in academic publishing where only reports of positive, rather than negative, results tend to be accepted, resulting in the effect known as publication bias..
- Data-snooping bias is a form of statistical bias that arises from this misuse of statistics.
- Although data-snooping bias can occur in any field that uses data mining, it is of particular concern in finance and medical research, which both heavily use data mining.
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- What type of sampling bias is likely to occur?
- Give an example of survivorship bias not presented in this text.