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Sampling error concerns natural variation between samples is always present and can be described using probability Is this true?
Wiki User
∙ 10y ago
No, it is not true. Probability can be used to describe some properties of the variation but not all.
Wiki User
∙ 10y ago
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What is the probability of selecting a king or queen?
It depends on the context which is not specified. If picking a topic for a history project on the monarchy of some country it would be 1! If it concerns selecting one card, at random, from a standard deck of playing cards, the answer is 8/52 = 2/13.
What are four kinds of sampling techniques?
Four sampling techniques are:1) Simple Random SamplingThis is the ideal choice as it is a 'perfect' random method. Using this method, individuals are randomly selected from a list of the population and every single individual has an equal chance of selection.This method is ideal, but if it cannot be adopted, one of the following alternatives may be chosen if any shortfall in accuracy.2) Systematic SamplingSystematic sampling is a frequently used variant of simple random sampling. When performing systematic sampling, every kth element from the list is selected (this is referred to as the sample interval) from a randomly selected starting point. For example, if we have a listed population of 6000 members and wish to draw a sample of 2000, we would select every 30th (6000 divided by 200) person from the list. In practice, we would randomly select a number between 1 and 30 to act as our starting point.The one potential problem with this method of sampling concerns the arrangement of elements in the list.? If the list is arranged in any kind of order e.g. if every 30th house is smaller than the others from which the sample is being recruited, there is a possibility that the sample produced could be seriously biased.3) Stratified SamplingStratified sampling is a variant on simple random and systematic methods and is used when there are a number of distinct subgroups, within each of which it is required that there is full representation. A stratified sample is constructed by classifying the population in sub-populations (or strata), base on some well-known characteristics of the population, such as age, gender or socio-economic status. The selection of elements is then made separately from within each strata, usually by random or systematic sampling methods.Stratified sampling methods also come in two types - proportionate and disproportionate.In proportionate sampling, the strata sample sizes are made proportional to the strata population sizes.For example if the first strata is made up of males, then as there are around 50% of males in the UK population, the male strata will need to represent around 50% of the total sample. In disproportionate methods, the strata are not sampled according to the population sizes, but higher proportions are selected from some groups and not others. This technique is typically used in a number of distinct situations:The costs of collecting data may differ from subgroup to subgroup.We might require more cases in some groups if estimations of populations values are likely to be harder to make i.e. the larger the sample size (up to certain limits), the more accurate any estimations are likely to be.We expect different response rates from different groups of people. Therefore, the less co-operative groups might be 'over-sampled' to compensate.4) Cluster or Multi-stage SamplingCluster sampling is a frequently-used, and usually more practical, random sampling method. It is particularly useful in situations for which no list of the elements within a population is available and therefore cannot be selected directly. As this form of sampling is conducted by randomly selecting subgroups of the population, possibly in several stages, it should produce results equivalent to a simple random sample.The sample is generally done by first sampling at the higher level(s) e.g. randomly sampled countries, then sampling from subsequent levels in turn e.g. within the selected countries sample counties, then within these postcodes, the within these households, until the final stage is reached, at which point the sampling is done in a simple random manner e.g. sampling people within the selected households. The 'levels' in question are defined by subgroups into which it is appropriate to subdivide your population.Cluster samples are generally used if:- No list of the population exists.- Well-defined clusters, which will often be geographic areas exist.- A reasonable estimate of the number of elements in each level of clustering can be made.- Often the total sample size must be fairly large to enable cluster sampling to be used effectively.Non-probability Sampling MethodsNon-probability sampling procedures are much less desirable, as they will almost certainly contain sampling biases. Unfortunately, in some circumstances such methods are unavoidable. In a Market Research context, the most frequently-adopted form of non-probability sampling is known as quota sampling.? In some ways this is similar to cluster sampling in that it requires the definition of key subgroups. The main difference lies in the fact that quotas (i.e. the amount of people to be surveyed) within subgroups are set beforehand (e.g. 25% 16-24 yr olds, 30% 25-34 yr olds, 20% 35-55 yr olds, and 25% 56+ yr olds) usually proportions are set to match known population distributions. Interviewers then select respondents according to these criteria rather than at random. The subjective nature of this selection means that only about a proportion of the population has a chance of being selected in a typical quota sampling strategy.If you are forced into using a non-random method, you must be extremely careful when drawing conclusions. You should always be honest about the sampling technique used and that a non-random approach will probably mean that biases are present within the data. In order to convert the sample to be representative of the true population, you may want to use weighting techniques.The importance of sampling should not be underestimated, as it determines to whom the results of your research will be applicable. It is important, therefore to give full consideration to the sampling strategy to be used and to select the most appropriate. Your most important consideration should be whether you could adopt a simple random sample.? If not, could one of the other random methods be used? Only when you have no choice should a non-random method be used.All to often, researchers succumb to the temptation of generalising their results to a much broader range of people than those from whom the data was originally gathered. This is poor practice and you should always aim to adopt an appropriate sampling technique. The key is not to guess, but take some advice.
There are 2 roads between town a and town b and 5 roads between town b and town c in how many ways can Pedro go from town a to town c thru town b and return without passing thru the same road twice?
A major manufacturing firm wants to open in your rural town. Your town currently has a population of fewer than five thousand people. Much of its land is undeveloped and heavily forested. The firm has submitted a proposal to clear a three-mile tract of land on which to build the plant. The town council is considering the proposal and is concerned about the consequences of clearing so much forest land to make room for industry. Their concerns are likely to include all of the following except _____.
What is so negative about negative binomial distribution?
Nothing really. It concerns an experiment with identified success and failure probabilities (p and q), or Bernoulli trials, like the conventional binomial distribution. In an negative binomial experiment, the experiment is stopped after "r" successes occur in n trials. Thus, there must be r-1 successes in the first n-1 trials, and the final trial must be a success. This stopping event causes a n-1 and r-1 terms to appear in the factorial expressions of the distribution, which I suspect is the origins of calling this distribution a "negative binomial distribution." I would prefer to call this a Bernoulli experiment distibution with a stopping rule, but that's probably much too long. Some excellent websites provide examples and more discussion: http://mathworld.wolfram.com/NegativeBinomialDistribution.html http://stattrek.com/Lesson2/NegBinomial.aspx http://en.wikipedia.org/wiki/Negative_binomial_distribution Stattrek has very good examples. Note the distribution can be expressed in a number of forms.
Statistics in the Workplace?
Mathematical data is an essential tool in the decision-making of organizations. Organizations in all fields employ statisticians who perform data analysis and help to move staff in a productive and efficient direction. College graduates with mathematical or scientific training may consider employment in statistics. Work as a statistician can be an immensely challenging experience that helps organizations to become larger and stronger. Statistics involve compiling and studying mathematical data related to the given organization. Statisticians use this data to make projections that can influence the path an organization will take. Organizational leaders rely on their statisticians to examine broad systems of operation and identify needed changes. There are various categories of statistics, depending upon the given field and organization type. Business statistics analyze a company�s productivity, marketing, employment methods and general cost-effectiveness. Epidemiology is the tracking of health-related events in a general population, such as births, accidents, and disease outbreaks. Biostatistics involve analysis for biological research and application. A statistician�s choice of field often coincides with his or her personal interests and concerns. Statisticians usually own advanced degrees in mathematics, applied sciences or information management. Senior level statisticians own extensive backgrounds in long-term and short-term statistical projects. All statisticians must have a clear sense of project organization, the ability to fulfill deadlines, and a sound methodology in carrying out their analyses. Students pursuing an analysis career must complete studies in advanced mathematics, computer programming and physical sciences such as chemistry and biology. Graduates often begin their careers as lower-level analysts and data assistants or in secondary employment such as lab support and teaching. As their work experience grows, they will eventually lead statistical projects and advance to senior analytic roles. Statistics are necessary tools in areas such as manufacturing, education, health care and social services. Changes are taking place more quickly and frequently in the workplace, which has increased the need for statisticians when taking informed action.
What do you understand by concept of probability?
Probability concerns with estimating a likelyhood for an event to either yet to happen or would have happened.
When the air quality index is in the 0 to 50 range how can the level of health concerns be described?
good
Can eating mold kill you?
It depends which mold in particular. So, the answer is -- maybe. Clearly you shouldn't risk it by sampling mold to see how it tastes. If in doubt, contact an authoritative agency in your area to discuss your mold concerns.
What books described an ideal socialist future in the US?
looking backward
Who is the saint for concern?
There are patron saints for health concerns, there are saints for family concerns, for financial concerns, etc. There is no generalist for concerns.
What are concerns about recycling?
what concerns are there about recycling?
What are the concerns of ecology?
concerns of ecology is a word ...
What animal has concerns?
The truth is ALL animals have concerns. Each one is different , but again they all have concerns.
What is the probability of selecting a king or queen?
It depends on the context which is not specified. If picking a topic for a history project on the monarchy of some country it would be 1! If it concerns selecting one card, at random, from a standard deck of playing cards, the answer is 8/52 = 2/13.
What are four kinds of sampling techniques?
Four sampling techniques are:1) Simple Random SamplingThis is the ideal choice as it is a 'perfect' random method. Using this method, individuals are randomly selected from a list of the population and every single individual has an equal chance of selection.This method is ideal, but if it cannot be adopted, one of the following alternatives may be chosen if any shortfall in accuracy.2) Systematic SamplingSystematic sampling is a frequently used variant of simple random sampling. When performing systematic sampling, every kth element from the list is selected (this is referred to as the sample interval) from a randomly selected starting point. For example, if we have a listed population of 6000 members and wish to draw a sample of 2000, we would select every 30th (6000 divided by 200) person from the list. In practice, we would randomly select a number between 1 and 30 to act as our starting point.The one potential problem with this method of sampling concerns the arrangement of elements in the list.? If the list is arranged in any kind of order e.g. if every 30th house is smaller than the others from which the sample is being recruited, there is a possibility that the sample produced could be seriously biased.3) Stratified SamplingStratified sampling is a variant on simple random and systematic methods and is used when there are a number of distinct subgroups, within each of which it is required that there is full representation. A stratified sample is constructed by classifying the population in sub-populations (or strata), base on some well-known characteristics of the population, such as age, gender or socio-economic status. The selection of elements is then made separately from within each strata, usually by random or systematic sampling methods.Stratified sampling methods also come in two types - proportionate and disproportionate.In proportionate sampling, the strata sample sizes are made proportional to the strata population sizes.For example if the first strata is made up of males, then as there are around 50% of males in the UK population, the male strata will need to represent around 50% of the total sample. In disproportionate methods, the strata are not sampled according to the population sizes, but higher proportions are selected from some groups and not others. This technique is typically used in a number of distinct situations:The costs of collecting data may differ from subgroup to subgroup.We might require more cases in some groups if estimations of populations values are likely to be harder to make i.e. the larger the sample size (up to certain limits), the more accurate any estimations are likely to be.We expect different response rates from different groups of people. Therefore, the less co-operative groups might be 'over-sampled' to compensate.4) Cluster or Multi-stage SamplingCluster sampling is a frequently-used, and usually more practical, random sampling method. It is particularly useful in situations for which no list of the elements within a population is available and therefore cannot be selected directly. As this form of sampling is conducted by randomly selecting subgroups of the population, possibly in several stages, it should produce results equivalent to a simple random sample.The sample is generally done by first sampling at the higher level(s) e.g. randomly sampled countries, then sampling from subsequent levels in turn e.g. within the selected countries sample counties, then within these postcodes, the within these households, until the final stage is reached, at which point the sampling is done in a simple random manner e.g. sampling people within the selected households. The 'levels' in question are defined by subgroups into which it is appropriate to subdivide your population.Cluster samples are generally used if:- No list of the population exists.- Well-defined clusters, which will often be geographic areas exist.- A reasonable estimate of the number of elements in each level of clustering can be made.- Often the total sample size must be fairly large to enable cluster sampling to be used effectively.Non-probability Sampling MethodsNon-probability sampling procedures are much less desirable, as they will almost certainly contain sampling biases. Unfortunately, in some circumstances such methods are unavoidable. In a Market Research context, the most frequently-adopted form of non-probability sampling is known as quota sampling.? In some ways this is similar to cluster sampling in that it requires the definition of key subgroups. The main difference lies in the fact that quotas (i.e. the amount of people to be surveyed) within subgroups are set beforehand (e.g. 25% 16-24 yr olds, 30% 25-34 yr olds, 20% 35-55 yr olds, and 25% 56+ yr olds) usually proportions are set to match known population distributions. Interviewers then select respondents according to these criteria rather than at random. The subjective nature of this selection means that only about a proportion of the population has a chance of being selected in a typical quota sampling strategy.If you are forced into using a non-random method, you must be extremely careful when drawing conclusions. You should always be honest about the sampling technique used and that a non-random approach will probably mean that biases are present within the data. In order to convert the sample to be representative of the true population, you may want to use weighting techniques.The importance of sampling should not be underestimated, as it determines to whom the results of your research will be applicable. It is important, therefore to give full consideration to the sampling strategy to be used and to select the most appropriate. Your most important consideration should be whether you could adopt a simple random sample.? If not, could one of the other random methods be used? Only when you have no choice should a non-random method be used.All to often, researchers succumb to the temptation of generalising their results to a much broader range of people than those from whom the data was originally gathered. This is poor practice and you should always aim to adopt an appropriate sampling technique. The key is not to guess, but take some advice.
What are the basic health concerns teenagers?
we dont have any concerns
What is the process by which spirtual concerns are replaced by worldly concerns?
evangelism
