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	Bias (statistics) - Wikipedia, the free encyclopedia
		A biased sample is a statistical sample in which members of the statistical population are not equally likely to be chosen.
		A biased estimator is one that for some reason on average over- or underestimates the quantity that is being estimated.
		A sample is biased if some members of the population are more likely to be chosen in the sample than others.
	en.wikipedia.org /wiki/Bias_(statistics) (779 words)

	Biased Samples
		The sample is biased because people living in rural areas or the country were excluded from the possibility of being included in the sample.
		The sample is biased because students not enrolled in Dr. Marx's classes are excluded from the chance of being included in the sample.
		This sample is biased because students who do not regularly check their email were excluded from the possibility of being in the survey.
	faculty.uncfsu.edu /jyoung/biased_samples.htm (856 words)

	Bias (statistik) - Wikipédia
		One meaning is involved in what is called a biased sample: If some elements are more likely to be chosen in the sample than others, and those that are have a higher or lower value of the quantity being estimated, the outcome will be higher or lower than the true value.
		Because their sample was biased towards wealthier citizens, their result was incorrect.
		This kind of bias is usually regarded as a worse problem than statistical noise: Problems with statistical noise can be lessened by enlarging the sample, but a biased sample will not go away that easily.
	su.wikipedia.org /wiki/Biased_estimator (694 words)

	[No title]
		A biased_ sample is a sample that is not representative of the entire population.
		Sampling with__ replacement is a procedure that requires a population member to be placed back into the population for further draws once it has been selected as a sample member.
		Samples formed when a limited pool is divided in such a way that each member of the pool has an equal chance of being assigned to any division and each set of members has an equal chance of forming any of the divisions are (a) random samples.
	www.susqu.edu /facstaff/m/misanin/Workbook/CH02WB.DOC (2986 words)

	Biased sample - Wikipedia, the free encyclopedia
		A biased sample is one that is falsely taken to be typical of a population from which it is drawn.
		Biased samples are not always an attempt to mislead: in 1936, in the early days of opinion polling, the American Literary Digest magazine called two million random telephone numbers, questioned the people who answered, and predicted the election result.
		They got it wrong because, at the time, telephones were far from universal, and telephone owners were not a good sample of the electorate as a whole.
	en.wikipedia.org /wiki/Biased_sample (406 words)

	Chapter 6 (Site not responding. Last check: 2007-11-07)
		Parameters (characteristics of a population) are estimated from statistics (characteristics of samples).
		The sample is randomly drawn from the population (usually accomplished using a table of random numbers).
		Samples are drawn from groups that are matched on some criteria or may involve two different sets of scores from the same group.
	faculty-staff.ou.edu /B/Nancy.H.Barry-1/sample.html (1029 words)

	ESGS Logical Fallacies (Site not responding. Last check: 2007-11-07)
		A sample is biased or loaded when the method used to take the sample is likely to result in a sample that does not adequately represent the population from which it is drawn.
		Random Sample: This is a sample that is taken in such a way that nothing but chance determines which members of the population are selected for the sample.
		This type of sample avoids being biased because a biased sample is one that is taken in such a way that some members of the population have a significantly greater chance of being selected for the sample than other members.
	www.esgs.org /fr/log35.htm (1134 words)

	Logical Fallacy: Unrepresentative Sample
		Moreover, since the strength of statistical inferences depend upon the similarity of the sample and population, they are really a species of argument from analogy, and the strength of the inference varies directly with the strength of the analogy.
		The sample is simply too small to represent the population, in which case the argument will commit the subfallacy of Hasty Generalization.
		The sample is biased in some way as a result of not having been chosen randomly from the population.
	www.fallacyfiles.org /biassamp.html (741 words)

	Biased sample
		A biased sample falsely claims to be typical of the whole group.
		Biased samples aren't always an attempt to mislead: in 1936, in the early days of opinion polling, the American Literary Digest magazine called two million random telephone numbers, questioned the people who answered, and predicted the election result.
		They got it wrong because, at the time, telephones were far from universal, and telephone owners weren't a good sample of the electorate as a whole.
	www.ebroadcast.com.au /lookup/encyclopedia/bi/Biased_sample.html (195 words)

	[No title]
		Sampling Techniques must be appropriate to ensure that inferences about the population are valid and that the collected data are unbiased.
		A Simple Random Sample is a sample in which every possible sample of the same size has the same chance of being selected.
		A Convenience Sample is a sample consisting of available members of a given population.
	www.selu.edu /Academics/Faculty/apearson/2-4HowtoCollecttheData.doc (773 words)

	Sample (Site not responding. Last check: 2007-11-07)
		A sample is a subset of a population.
		Since it is usually impractical to test every member of a population, a sample from the population is typically the best approach available.
		Inferential statistics generally require that sampling be random although some types of sampling (such as those used in voter polling) seek to make the sample as representative of the population as possible by choosing the sample to resemble the population on the most important characteristics.
	davidmlane.com /hyperstat/A10931.html (76 words)

	[No title] (Site not responding. Last check: 2007-11-07)
		Sample imprecision arises when a sample is taken that is not representative of the bulk material from which it came.
		In most situations the sample taken for analysis is not the sample actual used for testing.
		The sample used for testing is a sub-sample of the initial sample, and it, too, is subject to significant sample imprecision.
	www.accuvin.com (573 words)

	Jan 11 (Site not responding. Last check: 2007-11-07)
		Defn: Representative sample - sample in which the relevant characteristics of the sample members are generally the same as the characteristics of the population.
		Biased collection methods can cause the raw data to be biased.
		Goal is to produce a representative sample because only representative samples lead to a successful study.
	www.olemiss.edu /courses/math115/jan_11.htm (408 words)

	Biased Sample (Site not responding. Last check: 2007-11-07)
		Sample S, which is biased, is taken from population P. Conclusion C is drawn about Population P based on S. The person committing the fallacy is misusing the following type of reasoning, which is known variously as Inductive Generalization, Generalization, and Statistical Generalization:
		Stratified Sample: This is a sample that is taken by using the following steps: 1) The relevant strata (population subgroups) are identified, 2) The number of members in each stratum is determined and 3) A random sample is taken from each stratum in exact proportion to its size.
		A prediction based on only one sample is likely to be a Hasty Generalization (because the sample is likely to be too small to cover past, present and future populations) or a Biased Sample (because the sample will only include instances from one time period).
	animalliberationfront.us /Philosophy/Debating/Logic/biased-sample.html (1049 words)

	Final Report on the Effects of Sample Attrition on Estimates of Channeling's Impacts Executive Summary
		The loss of sample members from the analysis samples entails--in addition to reduction in sample sizes--the risk that sample members remaining in the treatment and control groups might differ on observed and unobserved characteristics, leading to biased estimates of channeling impacts.
		The proportion of the full sample included in the various analysis samples was nearly always substantially lower for the control group than for the treatment group in all three time periods, especially in the financial control model.
		The procedure that was used required the estimation of a model to predict whether the sample member was in the analysis sample (using all of the observations), and then the use of the estimated model to construct a new variable for each member of the analysis sample.
	aspe.hhs.gov /daltcp/reports/ATRITNES.HTM (899 words)

	Although I have noticed a trend between height and weight it is not completely clear, this may be because the data is ...
		Below is a short sample of the essay "Although I have noticed a trend between height and weight it is not completely clear, this may be because the data is rather biased.".
		The evidence from the sample suggests that 3 out of 9 or 33% of the girls are between 160cm and 170cm, whilst 4 out of 9 boys or 44% are of the same height, between 160cm and 170cm.
		In the stratified sample there was a large spread between the smallest and tallest boy; there were two who were 142cm tall this is a 11cm difference from the mean.
	www.coursework.info /i/20780.html (1608 words)

	Introduction to Sampling (Site not responding. Last check: 2007-11-07)
		Sampling represents the problem of accurately acquiring the necessary data in order to form a representative view of the problem.
		The sample frame is the list of people (`objects' for inanimate populations) that make up the target population; it is a list of the individuals who meet the `requirements' to be a member of that population.
		The sample is selected from the sample frame by specifying the sample size (either as a finite number, or as a proportion of the population) and the sampling method (the process by which we choose the members of the sample).
	zebu.uoregon.edu /1996/es202/l1.html (236 words)

	Jeopardy Review
		Answer: this sampling method begins by identifying a set of subgroups within a population, next researchers determine what proportion of the population corresponds to each subgroup, and finally, a sample is obtained such that it matches these exact population proportions.
		Answer: In this type of sampling, the odds of selecting a particular individual are not known because the researcher does not know the population size or the members of the population.
		Answer: This method requires that an individual selected for the sample to be recorded as a sample member and then returned to the population before the next selection is made, thus ensuring each member of the population has an equal probability of being selected to be in the sample each time a selection is made.
	www.dana.edu /bzimmerm/methods/j2.html (999 words)

	NAWQA Nutrients Data: Explanation of Stream Data
		Generally, samples were collected weekly or semi-weekly during periods when pesticide levels were expected to be highest and monthly or semi-monthly during the remainder of the year.
		Samples at the BFS were analyzed for the same non-pesticide constituents as samples from the IFS.
		Sampling at the intensive fixed sites was more frequent during periods when pesticide levels were expected to be elevated.
	water.usgs.gov /nawqa/nutrients/datasets/cycle91/swexp.htm (1032 words)

	Logical Fallacy: Hasty Generalization
		With a completely homogeneous population, a sample of one is sufficiently large, so it is impossible to put an absolute lower limit on sample size.
		Rather, sample size depends directly upon the variability of the population: the more heterogeneous a population, the larger the sample required.
		Here, your sample is one strand of spaghetti, and the population is the entire potful of pasta.
	www.fallacyfiles.org /hastygen.html (770 words)

	The American Statistician: Mean streets: the median of a size-biased sample and the population mean. (Teacher's ... (Site not responding. Last check: 2007-11-07)
		In a size-biased or weighted sample, the probability that a unit is sampled depends on a property of the sampling unit.
		Cars sampled by an observer traveling at a fixed velocity, [v.sub.0], can be modeled as a size-biased sample because a car with a large absolute difference between its velocity and [v.sub.0] is more likely to be sampled.
		A consistent estimator of the population mean velocity is found that depends solely on counts of the number of cars the observer passes and the number of cars that pass the observer.
	highbeam.com /library/doc0.asp?DOCID=1G1:15636063&refid=ip_almanac_hf (206 words)

	Math-502 (Site not responding. Last check: 2007-11-07)
		The average of large number of a function of a random sample estimate the expected value of that function.
		For each sample, the unbiased sample variance and the biased sample sample variance are obtained.
		We see that the biased sample variance has a smaller mse than the unbiased sample variance.
	www.math.binghamton.edu /arcones/502/2.html (186 words)

	Chapter 7: Sampling Distributions (Site not responding. Last check: 2007-11-07)
		A random sample is one in which all members of the population have an equal chance at getting selected as members of the sample.
		A biased sample is one obtained by a method that systematically underselects or overselects from certain groups in the population, thus all population members do not have an equal chance of selection.
		A sampling distribution is a distribution of samples’ statistics.
	www.tarleton.edu /~mkerr/5003/Ch7_5003.htm (1081 words)

	Using a political example, we can see the effects of a study based on a biased sample. In 1936, a popular magazine, The ... (Site not responding. Last check: 2007-11-07)
		For the chemist testing the stack emissions, the population is all 20 million cubic feet of stack gases and the sample is the 1 cubic foot collected for analysis.
		The leftmost bin is the number of samples that were less than or equal to 0.72 and the rightmost bin is the number of samples that were greater than 0.92 and less than or equal to 0.97.
		As discussed previously, this means that three samples had a fluoride level that was greater than.72 ppm and less than or equal to.77 ppm.
	owl.ben.edu /departments/science/NatSciLab/statistics/statistics.html (4533 words)

	Journal of General Psychology: Statistical Significance Levels of Nonparametric Tests Biased by Heterogeneous Variances ... (Site not responding. Last check: 2007-11-07)
		More recently, however, it has become apparent that these tests also are biased when sample sizes are unequal (see, for example, Zimmerman, 1996; Zimmerman & Zumbo, 1993), although the bias is not as extreme as that of their parametric counterparts.
		In fact, their significance levels are biased to a far greater extent than those of the corresponding parametric tests, the Student t test and the independent-groups ANOVA F test.
		The ratio of standard deviations of the two groups was varied by multiplying all scores in one sample by a constant, and the means of the two groups remained equal for all ratios of standard deviations.
	www.findarticles.com /p/articles/mi_m2405/is_4_127/ai_68025177 (1368 words)

	Beacon Lesson Plan Library
		A systematic sample is acquired by selecting one member of the population on a random basis and by choosing additional members at evenly spaced intervals until the desired number for the sample space has been collected.
		When the method used to acquire a sample results in a sample that is systematically different from the population, it is called a biased sample.
		Technically, this is a biased sample because it is the method used to create the sample, not the actual make up of the sample itself that defines the bias.
	www.beaconlearningcenter.com /search/details.asp?item=1564 (2433 words)

	FRBB: Economic Quiz
		The sample was biased towards individuals with higher incomes.
		The sample was biased in some way and not representative of the actual population
		By simple chance, the majority opinion of this sample is different from that of the overall population.
	www.bos.frb.org /economic/quiz/q0404.cfm (359 words)

	Bias (Site not responding. Last check: 2007-11-07)
		A statistic is biased if, in the long run, it consistently over or underestimates the parameter it is estimating.
		A statistic is positively biased if it tends to overestimate the parameter; a statistic is negatively biased if it tends to underestimate the parameter.
		A slightly biased statistic that systematically results in very small overestimates of a parameter could be quite efficient.
	davidmlane.com /hyperstat/A9257.html (131 words)

	GMAT: Critical Reasoning Chapter 3
		The Fallacy of the Biased Sample is committed whenever the data for a statistical inference is drawn from a sample that is not representative of the population under consideration.
		The data for the inference in this argument is drawn from a sample that is not representative of the entire electorate.
		The Fallacy of the Insufficient Sample is committed whenever an inadequate sample is used to justify the conclusion drawn.
	www.800score.com /guidec3view1d.html (1289 words)

	Biased sample -- Facts, Info, and Encyclopedia article (Site not responding. Last check: 2007-11-07)
		In (A branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters) statistics, the word bias has divergent meanings.
		Some forms of statistical bias are very bad; others can have good effects in some cases; see (Click link for more info and facts about bias (statistics)) bias (statistics).
		A biased sample is one that is falsely taken to be typical of a ((statistics) the entire aggregation of items from which samples can be drawn) population from which it is drawn.
	www.absoluteastronomy.com /encyclopedia/B/Bi/Biased_sample.htm (455 words)

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