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Sampling Error Is Systematic

the sample does not include all members of the population, statistics on the sample, such as means and quantiles, generally differ from the characteristics of the entire population, sampling error example which are known as parameters. For example, if one measures the height of

Non Sampling Error

a thousand individuals from a country of one million, the average height of the thousand is typically not the types of sampling errors same as the average height of all one million people in the country. Since sampling is typically done to determine the characteristics of a whole population, the difference between the sample sampling error formula and population values is considered a sampling error.[1] Exact measurement of sampling error is generally not feasible since the true population values are unknown; however, sampling error can often be estimated by probabilistic modeling of the sample. Contents 1 Description 1.1 Random sampling 1.2 Bias problems 1.3 Non-sampling error 2 See also 3 Citations 4 References 5 External links Description[edit] Random sampling[edit]

Systematic Error

Main article: Random sampling In statistics, sampling error is the error caused by observing a sample instead of the whole population.[1] The sampling error is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter (Burns & Grove, 2009). An estimate of a quantity of interest, such as an average or percentage, will generally be subject to sample-to-sample variation.[1] These variations in the possible sample values of a statistic can theoretically be expressed as sampling errors, although in practice the exact sampling error is typically unknown. Sampling error also refers more broadly to this phenomenon of random sampling variation. Random sampling, and its derived terms such as sampling error, imply specific procedures for gathering and analyzing data that are rigorously applied as a method for arriving at results considered representative of a given population as a whole. Despite a common misunderstanding, "random" does not mean the same thing as "chance" as this idea is often used in describing situations of uncertainty, nor is it the same as projections based on an assessed probability or frequen

of causes of random errors are: electronic noise in the circuit of an electrical instrument, irregular changes in the heat loss rate from a solar collector due to changes in the wind. Random errors often have a Gaussian normal distribution (see Fig. 2). In such cases random sampling error definition statistical methods may be used to analyze the data. The mean m of a number sampling error and nonsampling error of measurements of the same quantity is the best estimate of that quantity, and the standard deviation s of the measurements shows the accuracy

How To Reduce Sampling Error

of the estimate. The standard error of the estimate m is s/sqrt(n), where n is the number of measurements. Fig. 2. The Gaussian normal distribution. m = mean of measurements. s = standard deviation of measurements. 68% of the https://en.wikipedia.org/wiki/Sampling_error measurements lie in the interval m - s < x < m + s; 95% lie within m - 2s < x < m + 2s; and 99.7% lie within m - 3s < x < m + 3s. The precision of a measurement is how close a number of measurements of the same quantity agree with each other. The precision is limited by the random errors. It may usually be determined by repeating the measurements. Systematic Errors Systematic http://www.physics.umd.edu/courses/Phys276/Hill/Information/Notes/ErrorAnalysis.html errors in experimental observations usually come from the measuring instruments. They may occur because: there is something wrong with the instrument or its data handling system, or because the instrument is wrongly used by the experimenter. Two types of systematic error can occur with instruments having a linear response: Offset or zero setting error in which the instrument does not read zero when the quantity to be measured is zero. Multiplier or scale factor error in which the instrument consistently reads changes in the quantity to be measured greater or less than the actual changes. These errors are shown in Fig. 1. Systematic errors also occur with non-linear instruments when the calibration of the instrument is not known correctly. Fig. 1. Systematic errors in a linear instrument (full line). Broken line shows response of an ideal instrument without error. Examples of systematic errors caused by the wrong use of instruments are: errors in measurements of temperature due to poor thermal contact between the thermometer and the substance whose temperature is to be found, errors in measurements of solar radiation because trees or buildings shade the radiometer. The accuracy of a measurement is how close the measurement is to the true value of the quantity being measured. The accuracy of measurements is often reduced by systematic errors, which are difficult to detect even for experienced research workers.

Taken from R. H. B. Exell, www.jgsee.kmutt.ac.th/exell/PracMath/ErrorAn.htm

the sample does not include all members of the population, statistics on the sample, such as means and quantiles, generally differ from the characteristics of the entire population, which https://en.wikipedia.org/wiki/Sampling_error are known as parameters. For example, if one measures the height of http://www.slideshare.net/rambhu21/sampling-and-sampling-errors-19870549 a thousand individuals from a country of one million, the average height of the thousand is typically not the same as the average height of all one million people in the country. Since sampling is typically done to determine the characteristics of a whole population, the difference between the sample sampling error and population values is considered a sampling error.[1] Exact measurement of sampling error is generally not feasible since the true population values are unknown; however, sampling error can often be estimated by probabilistic modeling of the sample. Contents 1 Description 1.1 Random sampling 1.2 Bias problems 1.3 Non-sampling error 2 See also 3 Citations 4 References 5 External links Description[edit] Random sampling[edit] Main sampling error is article: Random sampling In statistics, sampling error is the error caused by observing a sample instead of the whole population.[1] The sampling error is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter (Burns & Grove, 2009). An estimate of a quantity of interest, such as an average or percentage, will generally be subject to sample-to-sample variation.[1] These variations in the possible sample values of a statistic can theoretically be expressed as sampling errors, although in practice the exact sampling error is typically unknown. Sampling error also refers more broadly to this phenomenon of random sampling variation. Random sampling, and its derived terms such as sampling error, imply specific procedures for gathering and analyzing data that are rigorously applied as a method for arriving at results considered representative of a given population as a whole. Despite a common misunderstanding, "random" does not mean the same thing as "chance" as this idea is often used in describing situations of uncertainty, nor is it the same as projections based on an assessed probability or frequency. S

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details. SlideShare Explore Search You Upload Login Signup Home Technology Education More Topics For Uploaders Get Started Tips & Tricks Tools SAMPLING AND SAMPLING ERRORS Upcoming SlideShare Loading in …5 × 1 1 of 73 Like this presentation? Why not share! Share Email Sampling Errors byNeeraj Kumar 1458views Errors in research byAbinesh Raja M 15708views Type i and type ii errors byp24ssp 7645views RESEARCH METHOD - SAMPLING byHafizah Hajimia 161803views sampling ppt bySwati Luthra 25808views Sampling and Sample Types bySunil Kumar 27288views Share SlideShare Facebook Twitter LinkedIn Google+ Email Email sent successfully! Embed Size (px) Start on Show related SlideShares at end WordPress Shortcode Link SAMPLING AND SAMPLING ERRORS 27,270 views Share Like Download rambhu21 Follow 0 0 0 Published on Apr 23, 2013 A GENERAL OUTLINE ... Published in: Education, Technology 2 Comments 40 Likes Statistics Notes Full Name Comment goes here. 12 hours ago Delete Reply Spam Block Are you sure you want to Yes No Your message goes here Post jama77 thanks want how to downloud for slides share 5 months ago Reply Are you sure you want to Yes No Your message goes here rambhu21 Thanks to all who like and use this. I request to all to share suggestion for the betterment. Again thanks to all 3 years ago Reply Are you sure you want to Yes No Your message goes here Nosheela Nazir 1 month ago Ghulam Mohyuddin , Assistant Professor at Govt.college of Education Afzalpur 2 months ago kanimozhi kanimozhi 2 months ago Azmat Hussain Shah Naqvi , Charge Nurse at Federal Government Poly Clinic (Post Graduate Medical Institute) Islamabad 3 months ago mohd shariq 5 months ago Show More No Downloads Views Total views 27,270 On SlideShare 0 From Embeds 0 Number of Embeds 27 Actions Shares 0 Downloads 1,374 Comments 2 Likes 40 Embeds 0 No embeds No notes for slide SAMPLING AND SAMPLING ERRORS 1. BYSHARADA(RESEARCH SCHOLAR)DEPTT. OF HOME SCIENCEMAHILA MAHA VIDYALAYABHU, VARANASISAMPLING: A Scientific Method ofData Collection 2. OUTLINE OF PRESENTATION SAMPLESAMPLINGSAMPLING METHODTYPES OF SAMPLING METHODSAMPLING ERROR 3. SAMPLE•It is a Unit that se

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sampling error can be reduced by
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