<doi_batch xmlns="http://www.crossref.org/schema/4.4.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="4.4.0"><head><doi_batch_id>b4d4897c-f8bf-4a43-9cab-a07dca3608d5</doi_batch_id><timestamp>20220323122119115</timestamp><depositor><depositor_name>wseas:wseas</depositor_name><email_address>mdt@crossref.org</email_address></depositor><registrant>MDT Deposit</registrant></head><body><journal><journal_metadata language="en"><full_title>WSEAS TRANSACTIONS ON MATHEMATICS</full_title><issn media_type="electronic">2224-2880</issn><issn media_type="print">1109-2769</issn><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/23206</doi><resource>http://wseas.org/wseas/cms.action?id=4051</resource></doi_data></journal_metadata><journal_issue><publication_date media_type="online"><month>1</month><day>5</day><year>2022</year></publication_date><publication_date media_type="print"><month>1</month><day>5</day><year>2022</year></publication_date><journal_volume><volume>21</volume><doi_data><doi>10.37394/23206.2022.21</doi><resource>https://wseas.com/journals/mathematics/2022.php</resource></doi_data></journal_volume></journal_issue><journal_article language="en"><titles><title>Unbiased Estimation of the Standard Deviation for Non-Normal Populations</title></titles><contributors><person_name sequence="first" contributor_role="author"><given_name>David E.</given_name><surname>Giles</surname><affiliation>Department of Economics University of Victoria, Victoria, B.C. CANADA</affiliation></person_name></contributors><jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1"><jats:p>The bias of the sample standard deviation as an estimator of the population standard deviation, for a simple random sample of size N from a Normal population, is well documented. Exact and approximate bias corrections appear in the literature for this case. However, there has been less discussion of the downward bias of this estimator for non-Normal populations. The appropriate bias correction depends on the kurtosis of the population distribution. We derive and illustrate an approximation for this bias, to O(N^( -1) ), for several common distributions.</jats:p></jats:abstract><publication_date media_type="online"><month>3</month><day>23</day><year>2022</year></publication_date><publication_date media_type="print"><month>3</month><day>23</day><year>2022</year></publication_date><pages><first_page>119</first_page><last_page>121</last_page></pages><publisher_item><item_number item_number_type="article_number">18</item_number></publisher_item><ai:program xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" name="AccessIndicators"><ai:free_to_read start_date="2022-03-23"/><ai:license_ref applies_to="am" start_date="2022-03-23">https://wseas.com/journals/mathematics/2022/a365106-005(2022).pdf</ai:license_ref></ai:program><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/23206.2022.21.18</doi><resource>https://wseas.com/journals/mathematics/2022/a365106-005(2022).pdf</resource></doi_data><citation_list><citation key="ref0"><doi>10.2307/2681802</doi><unstructured_citation>Bolch, B.W., More on Unbiased Estimation of the Standard Deviation, The American Statistician, 22 (3), 1968, 27. </unstructured_citation></citation><citation key="ref1"><doi>10.2307/2681734</doi><unstructured_citation>Brugger, R.M., A Note on Unbiased Estimation of the Standard Deviation, The American Statistician, 23 (4), 1969, 32. </unstructured_citation></citation><citation key="ref2"><doi>10.2307/2681876</doi><unstructured_citation>Cureton, E.E., Unbiased Estimation of the Standard Deviation, The American Statistician, 22 (1), 1968, 22. </unstructured_citation></citation><citation key="ref3"><doi>10.2307/2682419</doi><unstructured_citation>D’Agostino, R.B., Linear Estimation of the Normal Distribution Standard Deviation, The American Statistician, 24 (3), 1970, 14–15. </unstructured_citation></citation><citation key="ref4"><doi>10.2307/2682923</doi><unstructured_citation>Gurland, J. and Tripathi, R.C., A Simple Approximation for Unbiased Estimation of the Standard Deviation, The American Statistician, 25 (4), 1971, 30-32. </unstructured_citation></citation><citation key="ref5"><doi>10.2307/2681801</doi><unstructured_citation>Markowitz, E., Minimum Mean-Square- Error Estimation of the Standard Deviation of the Normal Distribution, The American Statistician, 22 (3), 1968, 26. </unstructured_citation></citation><citation key="ref6"><doi>10.2307/2681731</doi><unstructured_citation>Stuart, A., Reduced Mean-Square-Error Estimation of 𝜎 𝑝 in Normal Samples, The American Statistician, 23 (4), 1969, 27. </unstructured_citation></citation><citation key="ref7"><unstructured_citation>Angelova, J.A., On Moments of Sample Mean and Variance, International Journal of Pure and Applied Mathematics, 79 (1), 2012, 67-85. </unstructured_citation></citation><citation key="ref8"><unstructured_citation>Jarrett, R.F., A Minor Exercise in History,” The American Statistician, 22 (3), 1968, 25. </unstructured_citation></citation><citation key="ref9"><doi>10.2307/1418879</doi><unstructured_citation>Holtzman, W.H., The Unbiased Estimate of the Population Variance and Standard Deviation,” American Journal of Psychology, 63 (4), 1950, 615-617. </unstructured_citation></citation><citation key="ref10"><doi>10.1080/00401706.1979.10489785</doi><unstructured_citation>Johnson, M.E. and Lowe Jr., V.W., “Bounds on the Sample Skewness and Kurtosis,” Technometrics, 21 (3), 1979, 377- 378.</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>