{"id":55859,"date":"2025-01-16T12:29:50","date_gmt":"2025-01-16T15:29:50","guid":{"rendered":"https:\/\/mindthegraph.com\/blog\/?p=55859"},"modified":"2025-01-23T12:43:07","modified_gmt":"2025-01-23T15:43:07","slug":"ascertainment-bias","status":"publish","type":"post","link":"https:\/\/mindthegraph.com\/blog\/cs\/ascertainment-bias\/","title":{"rendered":"P\u0159edpojatost p\u0159i zji\u0161\u0165ov\u00e1n\u00ed: jak ji rozpoznat a p\u0159edch\u00e1zet j\u00ed ve v\u00fdzkumu"},"content":{"rendered":"<p>Zkreslen\u00ed zji\u0161\u0165ov\u00e1n\u00ed je b\u011b\u017en\u00fdm probl\u00e9mem ve v\u00fdzkumu, ke kter\u00e9mu doch\u00e1z\u00ed, kdy\u017e shrom\u00e1\u017ed\u011bn\u00e9 \u00fadaje nereprezentuj\u00ed p\u0159esn\u011b celou situaci. Pochopen\u00ed zkreslen\u00ed zji\u0161\u0165ov\u00e1n\u00ed m\u00e1 z\u00e1sadn\u00ed v\u00fdznam pro zlep\u0161en\u00ed spolehlivosti \u00fadaj\u016f a zaji\u0161t\u011bn\u00ed p\u0159esn\u00fdch v\u00fdsledk\u016f v\u00fdzkumu. A\u010dkoli se n\u011bkdy uk\u00e1\u017ee jako u\u017eite\u010dn\u00e9, ne v\u017edy je to tak.&nbsp;<\/p>\n\n\n\n<p>Ke zkreslen\u00ed doch\u00e1z\u00ed tehdy, kdy\u017e shrom\u00e1\u017ed\u011bn\u00e9 \u00fadaje neodr\u00e1\u017eej\u00ed pravdiv\u011b celou situaci, proto\u017ee ur\u010dit\u00e9 typy \u00fadaj\u016f se shroma\u017e\u010fuj\u00ed s v\u011bt\u0161\u00ed pravd\u011bpodobnost\u00ed ne\u017e jin\u00e9. To m\u016f\u017ee zkreslit v\u00fdsledky a poskytnout v\u00e1m zkreslenou p\u0159edstavu o tom, co se skute\u010dn\u011b d\u011bje.<\/p>\n\n\n\n<p>M\u016f\u017ee to zn\u00edt matouc\u00ed, ale pochopen\u00ed zkreslen\u00ed zji\u0161t\u011bn\u00ed v\u00e1m pom\u016f\u017ee b\u00fdt kriti\u010dt\u011bj\u0161\u00ed k \u00fadaj\u016fm, se kter\u00fdmi pracujete, a va\u0161e v\u00fdsledky tak budou spolehliv\u011bj\u0161\u00ed. V tomto \u010dl\u00e1nku se touto zkreslenost\u00ed budeme zab\u00fdvat do hloubky a vysv\u011btl\u00edme v\u0161e, co s n\u00ed souvis\u00ed. Proto se bez ot\u00e1len\u00ed pus\u0165me do toho!<\/p>\n\n\n\n<h2>Pochopen\u00ed zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed v\u00fdsledk\u016f v\u00fdzkumu<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" loading=\"lazy\" width=\"1024\" height=\"683\" src=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/nordwood-themes-EZSm8xRjnX0-unsplash-1024x683.jpg\" alt=\"Detailn\u00ed z\u00e1b\u011br na ruce p\u00ed\u0161\u00edc\u00ed na notebooku se zelenou rostlinou v kv\u011btin\u00e1\u010di na b\u00edl\u00e9m stole v \u010dist\u00e9m a minimalistick\u00e9m pracovn\u00edm prostoru.\" class=\"wp-image-55862\" srcset=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/nordwood-themes-EZSm8xRjnX0-unsplash-1024x683.jpg 1024w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/nordwood-themes-EZSm8xRjnX0-unsplash-300x200.jpg 300w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/nordwood-themes-EZSm8xRjnX0-unsplash-768x512.jpg 768w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/nordwood-themes-EZSm8xRjnX0-unsplash-1536x1024.jpg 1536w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/nordwood-themes-EZSm8xRjnX0-unsplash-2048x1365.jpg 2048w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/nordwood-themes-EZSm8xRjnX0-unsplash-18x12.jpg 18w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/nordwood-themes-EZSm8xRjnX0-unsplash-100x67.jpg 100w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Foto de <a href=\"https:\/\/unsplash.com\/pt-br\/@nordwood?utm_content=creditCopyText&#038;utm_medium=referral&#038;utm_source=unsplash\">T\u00e9mata spole\u010dnosti NordWood<\/a> na <a href=\"https:\/\/unsplash.com\/pt-br\/fotografias\/pessoa-usando-laptop-EZSm8xRjnX0?utm_content=creditCopyText&#038;utm_medium=referral&#038;utm_source=unsplash\">Unsplash<\/a>\n      <\/figcaption><\/figure>\n\n\n\n<p>Zkreslen\u00ed zji\u0161\u0165ov\u00e1n\u00ed vznik\u00e1, kdy\u017e metody sb\u011bru dat up\u0159ednost\u0148uj\u00ed ur\u010dit\u00e9 informace, co\u017e vede ke zkreslen\u00fdm a ne\u00fapln\u00fdm z\u00e1v\u011br\u016fm. Pokud si uv\u011bdom\u00edte, jak zkreslen\u00ed zji\u0161\u0165ov\u00e1n\u00ed ovliv\u0148uje v\u00e1\u0161 v\u00fdzkum, m\u016f\u017eete podniknout kroky k minimalizaci jeho dopadu a zv\u00fd\u0161it validitu sv\u00fdch zji\u0161t\u011bn\u00ed. Doch\u00e1z\u00ed k n\u00ed tehdy, kdy\u017e je pravd\u011bpodobn\u011bj\u0161\u00ed, \u017ee n\u011bkter\u00e9 informace budou shrom\u00e1\u017ed\u011bny, zat\u00edmco jin\u00e9 d\u016fle\u017eit\u00e9 \u00fadaje budou vynech\u00e1ny.&nbsp;<\/p>\n\n\n\n<p>V d\u016fsledku toho m\u016f\u017eete vyvodit z\u00e1v\u011bry, kter\u00e9 neodpov\u00eddaj\u00ed skute\u010dnosti. Pochopen\u00ed tohoto zkreslen\u00ed je nezbytn\u00e9 pro zaji\u0161t\u011bn\u00ed p\u0159esnosti a spolehlivosti va\u0161ich zji\u0161t\u011bn\u00ed nebo pozorov\u00e1n\u00ed.<\/p>\n\n\n\n<p>Zjednodu\u0161en\u011b \u0159e\u010deno, zkreslen\u00ed zji\u0161t\u011bn\u00ed znamen\u00e1, \u017ee to, na co se d\u00edv\u00e1te, v\u00e1m neposkytuje \u00faplnou informaci. P\u0159edstavte si, \u017ee zkoum\u00e1te po\u010det lid\u00ed, kte\u0159\u00ed nos\u00ed br\u00fdle, pomoc\u00ed pr\u016fzkumu v ordinaci optometristy.&nbsp;<\/p>\n\n\n\n<p>Je pravd\u011bpodobn\u011bj\u0161\u00ed, \u017ee se tam setk\u00e1te s lidmi, kte\u0159\u00ed pot\u0159ebuj\u00ed korekci zraku, tak\u017ee va\u0161e \u00fadaje by byly zkreslen\u00e9, proto\u017ee nezohled\u0148ujete lidi, kte\u0159\u00ed optometristu nenav\u0161t\u011bvuj\u00ed. To je p\u0159\u00edklad zkreslen\u00ed zji\u0161t\u011bn\u00ed.<\/p>\n\n\n\n<p>Tato p\u0159edpojatost se m\u016f\u017ee vyskytovat v mnoha oblastech, nap\u0159\u00edklad ve zdravotnictv\u00ed, v\u00fdzkumu, a dokonce i v ka\u017edodenn\u00edm rozhodov\u00e1n\u00ed. Pokud se zam\u011b\u0159\u00edte pouze na ur\u010dit\u00e9 typy dat nebo informac\u00ed, m\u016f\u017eete p\u0159ehl\u00e9dnout jin\u00e9 kl\u00ed\u010dov\u00e9 faktory.&nbsp;<\/p>\n\n\n\n<p>Nap\u0159\u00edklad studie o ur\u010dit\u00e9 nemoci m\u016f\u017ee b\u00fdt zkreslen\u00e1, pokud jsou v nemocnic\u00edch sledov\u00e1ny pouze nejz\u00e1va\u017en\u011bj\u0161\u00ed p\u0159\u00edpady a zanedb\u00e1vaj\u00ed se leh\u010d\u00ed p\u0159\u00edpady, kter\u00e9 nejsou odhaleny. V d\u016fsledku toho se m\u016f\u017ee zd\u00e1t, \u017ee nemoc je z\u00e1va\u017en\u011bj\u0161\u00ed nebo roz\u0161\u00ed\u0159en\u011bj\u0161\u00ed, ne\u017e ve skute\u010dnosti je.<\/p>\n\n\n\n<h2>B\u011b\u017en\u00e9 p\u0159\u00ed\u010diny zkreslen\u00ed zji\u0161t\u011bn\u00ed<\/h2>\n\n\n\n<p>P\u0159\u00ed\u010diny zkreslen\u00ed zji\u0161\u0165ov\u00e1n\u00ed sahaj\u00ed od selektivn\u00edho v\u00fdb\u011bru vzorku a\u017e po zkreslen\u00ed v\u00fdkaznictv\u00ed a ka\u017ed\u00e1 z nich p\u0159isp\u00edv\u00e1 ke zkreslen\u00ed \u00fadaj\u016f jedine\u010dn\u00fdm zp\u016fsobem. N\u00ed\u017ee jsou uvedeny n\u011bkter\u00e9 z b\u011b\u017en\u00fdch d\u016fvod\u016f, pro\u010d k tomuto zkreslen\u00ed doch\u00e1z\u00ed:<\/p>\n\n\n\n<h3>Selektivn\u00ed odb\u011br vzork\u016f<\/h3>\n\n\n\n<p>Pokud si ke studiu vyberete pouze ur\u010ditou skupinu osob nebo \u00fadaj\u016f, riskujete, \u017ee vylou\u010d\u00edte dal\u0161\u00ed d\u016fle\u017eit\u00e9 informace. Pokud nap\u0159\u00edklad pr\u016fzkum zahrnuje pouze odpov\u011bdi lid\u00ed, kte\u0159\u00ed pou\u017e\u00edvaj\u00ed ur\u010dit\u00fd produkt, nebude reprezentovat n\u00e1zory t\u011bch, kte\u0159\u00ed jej nepou\u017e\u00edvaj\u00ed. To vede ke zkreslen\u00fdm z\u00e1v\u011br\u016fm, proto\u017ee neu\u017eivatel\u00e9 jsou z procesu sb\u011bru dat vynech\u00e1ni.<\/p>\n\n\n\n<h2>Metody detekce<\/h2>\n\n\n\n<p>N\u00e1stroje nebo metody pou\u017eit\u00e9 ke sb\u011bru dat mohou rovn\u011b\u017e zp\u016fsobit zkreslen\u00ed zji\u0161t\u011bn\u00ed. Pokud nap\u0159\u00edklad zkoum\u00e1te zdravotn\u00ed stav, ale pou\u017e\u00edv\u00e1te pouze testy, kter\u00e9 odhaluj\u00ed z\u00e1va\u017en\u00e9 p\u0159\u00edznaky, vynech\u00e1te p\u0159\u00edpady, kdy jsou p\u0159\u00edznaky m\u00edrn\u00e9 nebo nezji\u0161t\u011bn\u00e9. To zkresl\u00ed v\u00fdsledky a stav se bude zd\u00e1t v\u00e1\u017en\u011bj\u0161\u00ed nebo roz\u0161\u00ed\u0159en\u011bj\u0161\u00ed, ne\u017e je.<\/p>\n\n\n\n<h2>Nastaven\u00ed studie<\/h2>\n\n\n\n<p>N\u011bkdy m\u016f\u017ee m\u00edsto prov\u00e1d\u011bn\u00ed studie v\u00e9st ke zkreslen\u00ed. Pokud nap\u0159\u00edklad studujete chov\u00e1n\u00ed ve\u0159ejnosti, ale pozorujete pouze lidi v ru\u0161n\u00e9 m\u011bstsk\u00e9 oblasti, va\u0161e data nebudou odr\u00e1\u017eet chov\u00e1n\u00ed lid\u00ed v klidn\u011bj\u0161\u00edm venkovsk\u00e9m prost\u0159ed\u00ed. To vede k ne\u00fapln\u00e9mu pohledu na celkov\u00e9 chov\u00e1n\u00ed, kter\u00e9 se sna\u017e\u00edte pochopit.<\/p>\n\n\n\n<h2>P\u0159edpojatost p\u0159i pod\u00e1v\u00e1n\u00ed zpr\u00e1v<\/h2>\n\n\n\n<p>Lid\u00e9 maj\u00ed tendenci hl\u00e1sit nebo sd\u00edlet informace, kter\u00e9 se jim zdaj\u00ed d\u016fle\u017eit\u011bj\u0161\u00ed nebo nal\u00e9hav\u011bj\u0161\u00ed. V l\u00e9ka\u0159sk\u00e9 studii mohou pacienti se z\u00e1va\u017en\u00fdmi p\u0159\u00edznaky \u010dast\u011bji vyhledat l\u00e9\u010dbu, zat\u00edmco pacienti s m\u00edrn\u00fdmi p\u0159\u00edznaky nemus\u00ed k l\u00e9ka\u0159i v\u016fbec j\u00edt. T\u00edm doch\u00e1z\u00ed ke zkreslen\u00ed \u00fadaj\u016f, proto\u017ee se p\u0159\u00edli\u0161 zam\u011b\u0159uj\u00ed na z\u00e1va\u017en\u00e9 p\u0159\u00edpady a p\u0159ehl\u00ed\u017eej\u00ed ty m\u00edrn\u00e9.<\/p>\n\n\n\n<figure class=\"wp-block-image alignwide size-full\"><a href=\"https:\/\/mindthegraph.com\/poster-maker\/?utm_source=blog&amp;utm_medium=banners&amp;utm_campaign=conversion\"><img decoding=\"async\" loading=\"lazy\" width=\"651\" height=\"174\" src=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/06\/mind-the-graph.png\" alt=\"&quot;Propaga\u010dn\u00ed banner pro Mind the Graph s n\u00e1pisem &quot;Vytv\u00e1\u0159ejte v\u011bdeck\u00e9 ilustrace bez n\u00e1mahy s Mind the Graph&quot;, kter\u00fd zd\u016fraz\u0148uje snadnost pou\u017eit\u00ed platformy.&quot;\" class=\"wp-image-54656\" srcset=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/06\/mind-the-graph.png 651w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/06\/mind-the-graph-300x80.png 300w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/06\/mind-the-graph-18x5.png 18w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/06\/mind-the-graph-100x27.png 100w\" sizes=\"(max-width: 651px) 100vw, 651px\" \/><\/a><figcaption class=\"wp-element-caption\">Vytv\u00e1\u0159ejte v\u011bdeck\u00e9 ilustrace bez n\u00e1mahy pomoc\u00ed <a href=\"https:\/\/mindthegraph.com\/poster-maker\/?utm_source=blog&amp;utm_medium=banners&amp;utm_campaign=conversion\">Mind the Graph<\/a>.<\/figcaption><\/figure>\n\n\n\n<h2>B\u011b\u017en\u00e9 situace, kdy m\u016f\u017ee doj\u00edt k p\u0159edpojatosti<\/h2>\n\n\n\n<p>Zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed se m\u016f\u017ee vyskytnout v r\u016fzn\u00fdch ka\u017edodenn\u00edch situac\u00edch a ve v\u00fdzkumn\u00fdch prost\u0159ed\u00edch:<\/p>\n\n\n\n<h3>Studie v oblasti zdravotn\u00ed p\u00e9\u010de<\/h3>\n\n\n\n<p>Pokud studie zahrnuje pouze \u00fadaje od pacient\u016f, kte\u0159\u00ed nav\u0161t\u00edv\u00ed nemocnici, m\u016f\u017ee nadhodnocovat z\u00e1va\u017enost nebo prevalenci onemocn\u011bn\u00ed, proto\u017ee opom\u00edj\u00ed ty, kte\u0159\u00ed maj\u00ed m\u00edrn\u00e9 p\u0159\u00edznaky a nevyhledaj\u00ed l\u00e9\u010dbu.<\/p>\n\n\n\n<h3>Pr\u016fzkumy a ankety<\/h3>\n\n\n\n<p>P\u0159edstavte si, \u017ee prov\u00e1d\u00edte pr\u016fzkum s c\u00edlem zjistit n\u00e1zory lid\u00ed na produkt, ale dotazujete se pouze st\u00e1vaj\u00edc\u00edch z\u00e1kazn\u00edk\u016f. Zp\u011btn\u00e1 vazba bude pravd\u011bpodobn\u011b pozitivn\u00ed, ale vynechali jste n\u00e1zory lid\u00ed, kte\u0159\u00ed produkt nepou\u017e\u00edvaj\u00ed. To m\u016f\u017ee v\u00e9st ke zkreslen\u00e9 p\u0159edstav\u011b o tom, jak produkt vn\u00edm\u00e1 \u0161irok\u00e1 ve\u0159ejnost.<\/p>\n\n\n\n<h3>Pozorovac\u00ed v\u00fdzkum<\/h3>\n\n\n\n<p>Pokud sledujete chov\u00e1n\u00ed zv\u00ed\u0159at, ale studujete pouze zv\u00ed\u0159ata v zoologick\u00e9 zahrad\u011b, va\u0161e data nebudou odr\u00e1\u017eet chov\u00e1n\u00ed t\u011bchto zv\u00ed\u0159at ve voln\u00e9 p\u0159\u00edrod\u011b. Omezen\u00e9 prost\u0159ed\u00ed zoologick\u00e9 zahrady m\u016f\u017ee zp\u016fsobovat jin\u00e9 chov\u00e1n\u00ed ne\u017e chov\u00e1n\u00ed pozorovan\u00e9 v jejich p\u0159irozen\u00e9m prost\u0159ed\u00ed.<\/p>\n\n\n\n<p>Rozpozn\u00e1n\u00edm a pochopen\u00edm t\u011bchto p\u0159\u00ed\u010din a p\u0159\u00edklad\u016f zkreslen\u00ed zji\u0161\u0165ov\u00e1n\u00ed m\u016f\u017eete podniknout kroky k zaji\u0161t\u011bn\u00ed p\u0159esn\u011bj\u0161\u00edho sb\u011bru a anal\u00fdzy dat. To v\u00e1m pom\u016f\u017ee vyhnout se vyvozov\u00e1n\u00ed zav\u00e1d\u011bj\u00edc\u00edch z\u00e1v\u011br\u016f a umo\u017en\u00ed v\u00e1m to l\u00e9pe porozum\u011bt re\u00e1ln\u00e9 situaci.<\/p>\n\n\n\n<h2>Jak identifikovat zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed v datech<\/h2>\n\n\n\n<p>Rozpozn\u00e1n\u00ed zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed zahrnuje identifikaci zdroj\u016f dat nebo metod, kter\u00e9 mohou ne\u00fam\u011brn\u011b zv\u00fdhod\u0148ovat ur\u010dit\u00e9 v\u00fdsledky oproti jin\u00fdm. Schopnost v\u010das odhalit zkreslen\u00ed zji\u0161t\u011bn\u00ed umo\u017e\u0148uje v\u00fdzkumn\u00fdm pracovn\u00edk\u016fm upravit sv\u00e9 metody a zajistit p\u0159esn\u011bj\u0161\u00ed v\u00fdsledky.<\/p>\n\n\n\n<p>Tato zaujatost se \u010dasto skr\u00fdv\u00e1 na o\u010d\u00edch a ovliv\u0148uje z\u00e1v\u011bry a rozhodnut\u00ed, ani\u017e by byla na prvn\u00ed pohled z\u0159ejm\u00e1. Kdy\u017e se nau\u010d\u00edte, jak ji rozpoznat, m\u016f\u017eete zv\u00fd\u0161it p\u0159esnost sv\u00e9ho v\u00fdzkumu a vyhnout se zav\u00e1d\u011bj\u00edc\u00edm p\u0159edpoklad\u016fm.<\/p>\n\n\n\n<h3>Zn\u00e1mky, kter\u00e9 je t\u0159eba hledat<\/h3>\n\n\n\n<p>Existuje n\u011bkolik ukazatel\u016f, kter\u00e9 v\u00e1m pomohou identifikovat zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed \u00fadaj\u016f. Uv\u011bdom\u011bn\u00ed si t\u011bchto p\u0159\u00edznak\u016f v\u00e1m umo\u017en\u00ed p\u0159ijmout opat\u0159en\u00ed a upravit metody sb\u011bru nebo anal\u00fdzy dat tak, abyste sn\u00ed\u017eili jejich dopad.<\/p>\n\n\n\n<h4>Selektivn\u00ed zdroje dat<\/h4>\n\n\n\n<p>Jedn\u00edm z nejz\u0159eteln\u011bj\u0161\u00edch p\u0159\u00edznak\u016f zkreslen\u00ed je, kdy\u017e \u00fadaje poch\u00e1zej\u00ed z omezen\u00e9ho nebo selektivn\u00edho zdroje.&nbsp;<\/p>\n\n\n\n<h4>Chyb\u011bj\u00edc\u00ed \u00fadaje<\/h4>\n\n\n\n<p>Dal\u0161\u00edm ukazatelem zkreslen\u00ed zji\u0161\u0165ov\u00e1n\u00ed jsou chyb\u011bj\u00edc\u00ed nebo ne\u00fapln\u00e9 \u00fadaje, zejm\u00e9na pokud jsou n\u011bkter\u00e9 skupiny nebo v\u00fdsledky nedostate\u010dn\u011b zastoupeny.&nbsp;<\/p>\n\n\n\n<h4>Nadm\u011brn\u00e9 zastoupen\u00ed ur\u010dit\u00fdch skupin<\/h4>\n\n\n\n<p>K p\u0159edpojatosti m\u016f\u017ee doj\u00edt tak\u00e9 tehdy, kdy\u017e je p\u0159i sb\u011bru dat nadm\u011brn\u011b zastoupena jedna skupina. \u0158ekn\u011bme, \u017ee zkoum\u00e1te pracovn\u00ed n\u00e1vyky v kancel\u00e1\u0159sk\u00e9m prost\u0159ed\u00ed a zam\u011b\u0159ujete se p\u0159edev\u0161\u00edm na vysoce v\u00fdkonn\u00e9 zam\u011bstnance. \u00dadaje, kter\u00e9 shrom\u00e1\u017ed\u00edte, by pravd\u011bpodobn\u011b nazna\u010dovaly, \u017ee dlouh\u00e1 pracovn\u00ed doba a p\u0159es\u010dasy vedou k \u00fasp\u011bchu. Ignorujete v\u0161ak ostatn\u00ed zam\u011bstnance, kte\u0159\u00ed mohou m\u00edt jin\u00e9 pracovn\u00ed n\u00e1vyky, co\u017e by mohlo v\u00e9st k nep\u0159esn\u00fdm z\u00e1v\u011br\u016fm o tom, co skute\u010dn\u011b p\u0159isp\u00edv\u00e1 k \u00fasp\u011bchu na pracovi\u0161ti.<\/p>\n\n\n\n<h4>Nekonzistentn\u00ed v\u00fdsledky nap\u0159\u00ed\u010d studiemi<\/h4>\n\n\n\n<p>Pokud si v\u0161imnete, \u017ee se v\u00fdsledky va\u0161\u00ed studie v\u00fdrazn\u011b li\u0161\u00ed od jin\u00fdch studi\u00ed na stejn\u00e9 t\u00e9ma, m\u016f\u017ee to b\u00fdt zn\u00e1mka toho, \u017ee se jedn\u00e1 o zkreslen\u00ed.<\/p>\n\n\n\n<p>&nbsp;<strong>P\u0159e\u010dt\u011bte si tak\u00e9: <\/strong><a href=\"https:\/\/mindthegraph.com\/blog\/publication-bias\/\"><strong>Publika\u010dn\u00ed p\u0159edsudky: v\u0161e, co pot\u0159ebujete v\u011bd\u011bt<\/strong><\/a><\/p>\n\n\n\n<h2>Dopad zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed<\/h2>\n\n\n\n<p>Zkreslen\u00ed zji\u0161t\u011bn\u00ed m\u016f\u017ee m\u00edt v\u00fdznamn\u00fd dopad na v\u00fdsledky v\u00fdzkumu, rozhodov\u00e1n\u00ed a politiky. Pochop\u00edte-li, jak toto zkreslen\u00ed ovliv\u0148uje v\u00fdsledky, m\u016f\u017eete l\u00e9pe ocenit v\u00fdznam jeho \u0159e\u0161en\u00ed v po\u010d\u00e1te\u010dn\u00ed f\u00e1zi sb\u011bru dat nebo procesu anal\u00fdzy.<\/p>\n\n\n\n<h3>Jak p\u0159edsudky ovliv\u0148uj\u00ed v\u00fdsledky v\u00fdzkumu<\/h3>\n\n\n\n<h4>Zkreslen\u00e9 z\u00e1v\u011bry<\/h4>\n\n\n\n<p>Nejz\u0159eteln\u011bj\u0161\u00edm dopadem zkreslen\u00ed zji\u0161t\u011bn\u00ed je, \u017ee vede ke zkreslen\u00fdm z\u00e1v\u011br\u016fm. Pokud jsou n\u011bkter\u00e9 datov\u00e9 body nadm\u011brn\u011b nebo nedostate\u010dn\u011b zastoupeny, v\u00fdsledky, kter\u00e9 z\u00edsk\u00e1te, nebudou p\u0159esn\u011b odr\u00e1\u017eet skute\u010dnost.&nbsp;<\/p>\n\n\n\n<h4>Nep\u0159esn\u00e9 p\u0159edpov\u011bdi<\/h4>\n\n\n\n<p>Pokud je v\u00fdzkum neobjektivn\u00ed, budou i p\u0159edpov\u011bdi na jeho z\u00e1klad\u011b nep\u0159esn\u00e9. V oblastech, jako je ve\u0159ejn\u00e9 zdrav\u00ed, mohou neobjektivn\u00ed \u00fadaje v\u00e9st k chybn\u00fdm p\u0159edpov\u011bd\u00edm o \u0161\u00ed\u0159en\u00ed nemoc\u00ed, \u00fa\u010dinnosti l\u00e9\u010dby nebo dopadu z\u00e1sah\u016f v oblasti ve\u0159ejn\u00e9ho zdrav\u00ed.<\/p>\n\n\n\n<h4>Neplatn\u00e1 zobecn\u011bn\u00ed<\/h4>\n\n\n\n<p>Jedn\u00edm z nejv\u011bt\u0161\u00edch nebezpe\u010d\u00ed zkreslen\u00ed zji\u0161t\u011bn\u00ed je, \u017ee m\u016f\u017ee v\u00e9st k neplatn\u00fdm zobecn\u011bn\u00edm. M\u016f\u017eete b\u00fdt v poku\u0161en\u00ed aplikovat v\u00fdsledky sv\u00e9 studie na \u0161ir\u0161\u00ed populaci, ale pokud byl v\u00e1\u0161 vzorek zkreslen\u00fd, va\u0161e z\u00e1v\u011bry nebudou platn\u00e9. To m\u016f\u017ee b\u00fdt obzvl\u00e1\u0161t\u011b \u0161kodliv\u00e9 v oborech, jako jsou soci\u00e1ln\u00ed v\u011bdy nebo vzd\u011bl\u00e1v\u00e1n\u00ed, kde se v\u00fdsledky v\u00fdzkumu \u010dasto pou\u017e\u00edvaj\u00ed k vytv\u00e1\u0159en\u00ed politik nebo intervenc\u00ed.<\/p>\n\n\n\n<h3>Mo\u017en\u00e9 d\u016fsledky v r\u016fzn\u00fdch oblastech<\/h3>\n\n\n\n<p>V z\u00e1vislosti na oboru studia nebo pr\u00e1ce m\u016f\u017ee m\u00edt zkreslen\u00ed zji\u0161t\u011bn\u00ed dalekos\u00e1hl\u00e9 d\u016fsledky. N\u00ed\u017ee uv\u00e1d\u00edme n\u011bkolik p\u0159\u00edklad\u016f, jak m\u016f\u017ee toto zkreslen\u00ed ovlivnit r\u016fzn\u00e9 oblasti:<\/p>\n\n\n\n<h4>Zdravotn\u00ed p\u00e9\u010de<\/h4>\n\n\n\n<p>Ve zdravotnictv\u00ed m\u016f\u017ee m\u00edt zkreslen\u00ed zji\u0161t\u011bn\u00ed z\u00e1va\u017en\u00e9 d\u016fsledky. Pokud se l\u00e9ka\u0159sk\u00e9 studie zam\u011b\u0159uj\u00ed pouze na z\u00e1va\u017en\u00e9 p\u0159\u00edpady onemocn\u011bn\u00ed, mohou l\u00e9ka\u0159i nadhodnocovat nebezpe\u010dnost nemoci. To m\u016f\u017ee v\u00e9st k nadm\u011brn\u00e9 l\u00e9\u010db\u011b nebo zbyte\u010dn\u00fdm z\u00e1sah\u016fm u pacient\u016f s m\u00edrn\u00fdmi p\u0159\u00edznaky. Na druhou stranu, pokud jsou m\u00edrn\u00e9 p\u0159\u00edpady podhodnoceny, poskytovatel\u00e9 zdravotn\u00ed p\u00e9\u010de nemus\u00ed br\u00e1t nemoc dostate\u010dn\u011b v\u00e1\u017en\u011b, co\u017e m\u016f\u017ee v\u00e9st k nedostate\u010dn\u00e9 l\u00e9\u010db\u011b.<\/p>\n\n\n\n<h4>Ve\u0159ejn\u00e1 politika<\/h4>\n\n\n\n<p>P\u0159i rozhodov\u00e1n\u00ed o ve\u0159ejn\u00e9m zdrav\u00ed, vzd\u011bl\u00e1v\u00e1n\u00ed a dal\u0161\u00edch d\u016fle\u017eit\u00fdch oblastech se politici \u010dasto spol\u00e9haj\u00ed na \u00fadaje. Pokud jsou \u00fadaje, kter\u00e9 pou\u017e\u00edvaj\u00ed, zkreslen\u00e9, mohou b\u00fdt politiky, kter\u00e9 vytv\u00e1\u0159ej\u00ed, ne\u00fa\u010dinn\u00e9 nebo dokonce \u0161kodliv\u00e9.&nbsp;<\/p>\n\n\n\n<h4>Obchodn\u00ed<\/h4>\n\n\n\n<p>V obchodn\u00edm sv\u011bt\u011b m\u016f\u017ee zkreslen\u00ed zji\u0161t\u011bn\u00ed v\u00e9st k chybn\u00e9mu pr\u016fzkumu trhu a \u0161patn\u00e9mu rozhodov\u00e1n\u00ed. Pokud spole\u010dnost prov\u00e1d\u00ed pr\u016fzkum pouze u sv\u00fdch nejv\u011brn\u011bj\u0161\u00edch z\u00e1kazn\u00edk\u016f, m\u016f\u017ee doj\u00edt k z\u00e1v\u011bru, \u017ee jej\u00ed v\u00fdrobky jsou v\u0161eobecn\u011b obl\u00edben\u00e9, zat\u00edmco ve skute\u010dnosti m\u016f\u017ee m\u00edt mnoho potenci\u00e1ln\u00edch z\u00e1kazn\u00edk\u016f negativn\u00ed n\u00e1zory. To by mohlo v\u00e9st k chybn\u00fdm marketingov\u00fdm strategi\u00edm nebo rozhodnut\u00edm o v\u00fdvoji produkt\u016f, kter\u00e1 nejsou v souladu s pot\u0159ebami \u0161ir\u0161\u00edho trhu.<\/p>\n\n\n\n<h4>Vzd\u011bl\u00e1v\u00e1n\u00ed<\/h4>\n\n\n\n<p>Ve vzd\u011bl\u00e1v\u00e1n\u00ed m\u016f\u017ee zkreslen\u00ed zji\u0161t\u011bn\u00ed ovlivnit v\u00fdzkum v\u00fdkonu student\u016f, v\u00fdukov\u00fdch metod nebo vzd\u011bl\u00e1vac\u00edch n\u00e1stroj\u016f. Pokud se studie zam\u011b\u0159uj\u00ed pouze na studenty s dobr\u00fdmi v\u00fdsledky, mohou p\u0159ehl\u00e9dnout probl\u00e9my, kter\u00fdm \u010del\u00ed studenti, kte\u0159\u00ed maj\u00ed probl\u00e9my, co\u017e vede k z\u00e1v\u011br\u016fm, kter\u00e9 se nevztahuj\u00ed na celou skupinu student\u016f. To by mohlo v\u00e9st k v\u00fdvoji vzd\u011bl\u00e1vac\u00edch program\u016f nebo politik, kter\u00e9 by nepodporovaly v\u0161echny studenty.<\/p>\n\n\n\n<p>Identifikace zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed je z\u00e1sadn\u00ed pro zaji\u0161t\u011bn\u00ed p\u0159esnosti a reprezentativnosti v\u00fdzkumu a z\u00e1v\u011br\u016f. Hled\u00e1n\u00edm p\u0159\u00edznak\u016f, jako jsou selektivn\u00ed zdroje dat, chyb\u011bj\u00edc\u00ed informace a nadm\u011brn\u00e9 zastoupen\u00ed ur\u010dit\u00fdch skupin, m\u016f\u017eete rozpoznat, kdy zkreslen\u00ed ovliv\u0148uje va\u0161e data.&nbsp;<\/p>\n\n\n\n<p><strong>P\u0159e\u010dt\u011bte si tak\u00e9: <\/strong><a href=\"https:\/\/mindthegraph.com\/blog\/observer-bias\/\"><strong>P\u0159ekon\u00e1n\u00ed p\u0159edsudk\u016f pozorovatele ve v\u00fdzkumu: Jak ji minimalizovat?<\/strong><\/a><\/p>\n\n\n\n<h2>Strategie pro zm\u00edrn\u011bn\u00ed zkreslen\u00ed zji\u0161t\u011bn\u00ed<\/h2>\n\n\n\n<p>Pokud chcete zajistit, aby data, se kter\u00fdmi pracujete, p\u0159esn\u011b reprezentovala realitu, kterou se sna\u017e\u00edte pochopit, je nezbytn\u00e9 zab\u00fdvat se zkreslen\u00edm zji\u0161t\u011bn\u00ed. Zkreslen\u00ed zji\u0161t\u011bn\u00ed se m\u016f\u017ee do va\u0161eho v\u00fdzkumu vpl\u00ed\u017eit, pokud jsou n\u011bkter\u00e9 typy dat zastoupeny nadm\u011brn\u011b nebo nedostate\u010dn\u011b, co\u017e vede ke zkreslen\u00fdm v\u00fdsledk\u016fm.&nbsp;<\/p>\n\n\n\n<p>Existuje v\u0161ak n\u011bkolik strategi\u00ed a technik, kter\u00e9 m\u016f\u017eete pou\u017e\u00edt ke zm\u00edrn\u011bn\u00ed tohoto zkreslen\u00ed a zv\u00fd\u0161en\u00ed spolehlivosti sb\u011bru a anal\u00fdzy dat.<\/p>\n\n\n\n<h3>Strategie pro zm\u00edrn\u011bn\u00ed p\u0159edsudk\u016f<\/h3>\n\n\n\n<p>Pokud se sna\u017e\u00edte minimalizovat zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed v\u00fdsledk\u016f ve sv\u00e9m v\u00fdzkumu nebo p\u0159i sb\u011bru dat, m\u016f\u017eete prov\u00e9st n\u011bkolik praktick\u00fdch krok\u016f a strategi\u00ed. Budete-li si v\u011bdomi mo\u017en\u00fdch zkreslen\u00ed a pou\u017eijete-li tyto techniky, m\u016f\u017eete sv\u00e1 data zp\u0159esnit a zv\u00fd\u0161it jejich reprezentativnost.<\/p>\n\n\n\n<h4>Pou\u017eit\u00ed n\u00e1hodn\u00e9ho v\u00fdb\u011bru vzork\u016f<\/h4>\n\n\n\n<p>Jedn\u00edm z nej\u00fa\u010dinn\u011bj\u0161\u00edch zp\u016fsob\u016f, jak sn\u00ed\u017eit zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed v\u00fdsledk\u016f, je pou\u017eit\u00ed <a href=\"https:\/\/mindthegraph.com\/blog\/simple-random-sampling\/\">n\u00e1hodn\u00fd v\u00fdb\u011br vzork\u016f<\/a>. T\u00edm je zaji\u0161t\u011bno, \u017ee ka\u017ed\u00fd \u010dlen populace m\u00e1 stejnou \u0161anci b\u00fdt do studie zahrnut, co\u017e pom\u00e1h\u00e1 zabr\u00e1nit nadm\u011brn\u00e9mu zastoupen\u00ed jedn\u00e9 skupiny.&nbsp;<\/p>\n\n\n\n<p>Pokud nap\u0159\u00edklad prov\u00e1d\u00edte pr\u016fzkum o stravovac\u00edch n\u00e1vyc\u00edch, n\u00e1hodn\u00fd v\u00fdb\u011br by zahrnoval n\u00e1hodn\u00fd v\u00fdb\u011br \u00fa\u010dastn\u00edk\u016f bez zam\u011b\u0159en\u00ed na konkr\u00e9tn\u00ed skupinu, jako jsou nap\u0159\u00edklad n\u00e1v\u0161t\u011bvn\u00edci posilovny nebo lid\u00e9, kte\u0159\u00ed ji\u017e dodr\u017euj\u00ed zdravou stravu. T\u00edmto zp\u016fsobem m\u016f\u017eete z\u00edskat p\u0159esn\u011bj\u0161\u00ed zastoupen\u00ed cel\u00e9 populace.<\/p>\n\n\n\n<p><strong>P\u0159e\u010dt\u011bte si tak\u00e9: <\/strong><a href=\"https:\/\/mindthegraph.com\/blog\/sampling-bias\/\"><strong>Probl\u00e9m zvan\u00fd zkreslen\u00ed v\u00fdb\u011bru vzorku<\/strong><\/a><\/p>\n\n\n\n<h4>Zv\u00fd\u0161en\u00ed rozmanitosti vzork\u016f<\/h4>\n\n\n\n<p>Dal\u0161\u00edm d\u016fle\u017eit\u00fdm krokem je zajistit, aby byl v\u00e1\u0161 vzorek r\u016fznorod\u00fd. To znamen\u00e1 aktivn\u011b vyhled\u00e1vat \u00fa\u010dastn\u00edky nebo zdroje dat z nejr\u016fzn\u011bj\u0161\u00edch prost\u0159ed\u00ed, zku\u0161enost\u00ed a podm\u00ednek. Pokud nap\u0159\u00edklad zkoum\u00e1te dopad nov\u00e9ho l\u00e9ku, ujist\u011bte se, \u017ee jsou do n\u011bj za\u0159azeni lid\u00e9 r\u016fzn\u00e9ho v\u011bku, pohlav\u00ed a zdravotn\u00edho stavu, abyste se nezam\u011b\u0159ili pouze na jednu skupinu. \u010c\u00edm rozmanit\u011bj\u0161\u00ed bude v\u00e1\u0161 vzorek, t\u00edm spolehliv\u011bj\u0161\u00ed budou va\u0161e z\u00e1v\u011bry.<\/p>\n\n\n\n<h4>Prov\u00e1d\u011bn\u00ed longitudin\u00e1ln\u00edch studi\u00ed<\/h4>\n\n\n\n<p>Longitudin\u00e1ln\u00ed studie je studie, kter\u00e1 sleduje \u00fa\u010dastn\u00edky po ur\u010ditou dobu a sb\u00edr\u00e1 \u00fadaje v n\u011bkolika bodech. Tento p\u0159\u00edstup v\u00e1m m\u016f\u017ee pomoci identifikovat jak\u00e9koli zm\u011bny nebo trendy, kter\u00e9 by mohly b\u00fdt p\u0159i jednor\u00e1zov\u00e9m sb\u011bru dat p\u0159ehl\u00e9dnuty. Sledov\u00e1n\u00edm \u00fadaj\u016f v pr\u016fb\u011bhu \u010dasu z\u00edsk\u00e1te \u00fapln\u011bj\u0161\u00ed obraz a sn\u00ed\u017e\u00edte pravd\u011bpodobnost zkreslen\u00ed, proto\u017ee v\u00e1m umo\u017en\u00ed sledovat, jak se faktory vyv\u00edjej\u00ed, a ne \u010dinit p\u0159edpoklady na z\u00e1klad\u011b jedin\u00e9ho sn\u00edmku.<\/p>\n\n\n\n<h4>Slep\u00e9 nebo dvojit\u011b slep\u00e9 studie<\/h4>\n\n\n\n<p>V n\u011bkter\u00fdch p\u0159\u00edpadech, zejm\u00e9na v l\u00e9ka\u0159sk\u00e9m nebo psychologick\u00e9m v\u00fdzkumu, je zaslepen\u00ed \u00fa\u010dinn\u00fdm zp\u016fsobem, jak sn\u00ed\u017eit zkreslen\u00ed. Jednoslep\u00e1 studie znamen\u00e1, \u017ee \u00fa\u010dastn\u00edci nev\u011bd\u00ed, do kter\u00e9 skupiny pat\u0159\u00ed (nap\u0159. zda dost\u00e1vaj\u00ed l\u00e9\u010dbu nebo placebo).&nbsp;<\/p>\n\n\n\n<p>Dvojit\u011b zaslepen\u00e1 studie jde je\u0161t\u011b o krok d\u00e1l, proto\u017ee zaji\u0161\u0165uje, \u017ee \u00fa\u010dastn\u00edci ani v\u00fdzkumn\u00edci nev\u011bd\u00ed, kdo je v jak\u00e9 skupin\u011b. To m\u016f\u017ee pomoci zabr\u00e1nit v\u011bdom\u00e9mu i nev\u011bdom\u00e9mu ovliv\u0148ov\u00e1n\u00ed v\u00fdsledk\u016f.<\/p>\n\n\n\n<h4>Pou\u017eit\u00ed kontroln\u00edch skupin<\/h4>\n\n\n\n<p>Zahrnut\u00ed kontroln\u00ed skupiny do studie v\u00e1m umo\u017en\u00ed porovnat v\u00fdsledky va\u0161\u00ed skupiny s t\u011bmi, kte\u0159\u00ed nebyli vystaveni intervenci. Toto srovn\u00e1n\u00ed v\u00e1m pom\u016f\u017ee ur\u010dit, zda jsou v\u00fdsledky zp\u016fsobeny samotnou intervenc\u00ed, nebo zda jsou ovlivn\u011bny jin\u00fdmi faktory. Kontroln\u00ed skupiny poskytuj\u00ed v\u00fdchoz\u00ed \u00farove\u0148, kter\u00e1 pom\u00e1h\u00e1 sn\u00ed\u017eit zkreslen\u00ed t\u00edm, \u017ee nab\u00edz\u00ed jasn\u011bj\u0161\u00ed p\u0159edstavu o tom, co by se stalo bez intervence.<\/p>\n\n\n\n<h4>Pilotn\u00ed studie<\/h4>\n\n\n\n<p>Proveden\u00ed pilotn\u00ed studie p\u0159ed zah\u00e1jen\u00edm v\u00fdzkumu v pln\u00e9m rozsahu v\u00e1m m\u016f\u017ee pomoci v\u010das identifikovat potenci\u00e1ln\u00ed zdroje zkreslen\u00ed zji\u0161\u0165ov\u00e1n\u00ed.&nbsp;<\/p>\n\n\n\n<p>Pilotn\u00ed studie je men\u0161\u00ed, zku\u0161ebn\u00ed verze va\u0161eho v\u00fdzkumu, kter\u00e1 v\u00e1m umo\u017en\u00ed otestovat va\u0161e metody a zjistit, zda v procesu sb\u011bru dat nejsou n\u011bjak\u00e9 nedostatky. M\u00e1te tak mo\u017enost prov\u00e9st \u00fapravy p\u0159edt\u00edm, ne\u017e se pust\u00edte do rozs\u00e1hlej\u0161\u00ed studie, a sn\u00ed\u017eit tak riziko zkreslen\u00ed kone\u010dn\u00fdch v\u00fdsledk\u016f.<\/p>\n\n\n\n<h4>Transparentn\u00ed pod\u00e1v\u00e1n\u00ed zpr\u00e1v<\/h4>\n\n\n\n<p>Transparentnost je kl\u00ed\u010dem k omezen\u00ed p\u0159edpojatosti. Otev\u0159en\u011b informujte o metod\u00e1ch sb\u011bru dat, technik\u00e1ch v\u00fdb\u011bru vzork\u016f a p\u0159\u00edpadn\u00fdch omezen\u00edch va\u0161\u00ed studie. T\u00edm, \u017ee jasn\u011b uvedete rozsah a omezen\u00ed, umo\u017en\u00edte ostatn\u00edm kriticky posoudit va\u0161i pr\u00e1ci a pochopit, kde mohou existovat zkreslen\u00ed. Tato up\u0159\u00edmnost pom\u00e1h\u00e1 budovat d\u016fv\u011bru a umo\u017e\u0148uje ostatn\u00edm replikovat v\u00e1\u0161 v\u00fdzkum nebo na n\u011bm stav\u011bt s p\u0159esn\u011bj\u0161\u00edmi \u00fadaji.<\/p>\n\n\n\n<h3>\u00daloha technologie<\/h3>\n\n\n\n<p>Technologie m\u016f\u017ee hr\u00e1t v\u00fdznamnou roli p\u0159i identifikaci a sni\u017eov\u00e1n\u00ed zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed. Pomoc\u00ed pokro\u010dil\u00fdch n\u00e1stroj\u016f a metod m\u016f\u017eete efektivn\u011bji analyzovat data, odhalit potenci\u00e1ln\u00ed zkreslen\u00ed a opravit je d\u0159\u00edve, ne\u017e ovlivn\u00ed va\u0161e z\u00e1v\u011bry.<\/p>\n\n\n\n<h4>Software pro anal\u00fdzu dat<\/h4>\n\n\n\n<p>Jedn\u00edm z nej\u00fa\u010dinn\u011bj\u0161\u00edch n\u00e1stroj\u016f pro sn\u00ed\u017een\u00ed zkreslen\u00ed je software pro anal\u00fdzu dat. Tyto programy dok\u00e1\u017e\u00ed rychle zpracovat velk\u00e9 mno\u017estv\u00ed dat a pomohou v\u00e1m identifikovat vzorce nebo nesrovnalosti, kter\u00e9 by mohly nazna\u010dovat zkreslen\u00ed.&nbsp;<\/p>\n\n\n\n<h4>Algoritmy strojov\u00e9ho u\u010den\u00ed<\/h4>\n\n\n\n<p>Algoritmy strojov\u00e9ho u\u010den\u00ed mohou b\u00fdt neuv\u011b\u0159iteln\u011b u\u017eite\u010dn\u00e9 p\u0159i odhalov\u00e1n\u00ed a oprav\u011b zkreslen\u00ed v datech. Tyto algoritmy lze vycvi\u010dit tak, aby rozpoznaly, kdy jsou ur\u010dit\u00e9 skupiny nedostate\u010dn\u011b zastoupeny nebo kdy jsou datov\u00e9 body zkreslen\u00e9 ur\u010dit\u00fdm sm\u011brem. Jakmile algoritmus identifikuje zkreslen\u00ed, m\u016f\u017ee podle toho upravit proces sb\u011bru nebo anal\u00fdzy dat, \u010d\u00edm\u017e zajist\u00ed, \u017ee kone\u010dn\u00e9 v\u00fdsledky budou p\u0159esn\u011bj\u0161\u00ed.<\/p>\n\n\n\n<h4>N\u00e1stroje pro automatizovan\u00fd sb\u011br dat<\/h4>\n\n\n\n<p>Automatizovan\u00e9 n\u00e1stroje pro sb\u011br dat mohou pomoci omezit lidsk\u00e9 chyby a zkreslen\u00ed b\u011bhem procesu sb\u011bru dat. Pokud nap\u0159\u00edklad prov\u00e1d\u00edte online pr\u016fzkum, m\u016f\u017eete pou\u017e\u00edt software, kter\u00fd n\u00e1hodn\u011b vybere \u00fa\u010dastn\u00edky nebo automaticky zajist\u00ed, aby byly do vzorku zahrnuty r\u016fzn\u00e9 skupiny.<\/p>\n\n\n\n<h4>Techniky statistick\u00e9 \u00fapravy<\/h4>\n\n\n\n<p>V n\u011bkter\u00fdch p\u0159\u00edpadech lze ke korekci zkreslen\u00ed po shrom\u00e1\u017ed\u011bn\u00ed \u00fadaj\u016f pou\u017e\u00edt metody statistick\u00e9 \u00fapravy. V\u00fdzkumn\u00ed pracovn\u00edci mohou nap\u0159\u00edklad pou\u017e\u00edt techniky, jako je v\u00e1\u017een\u00ed nebo imputace, aby upravili \u00fadaje o nedostate\u010dn\u011b zastoupen\u00e9 skupiny. V\u00e1\u017een\u00ed spo\u010d\u00edv\u00e1 v tom, \u017ee se \u00fadaj\u016fm z nedostate\u010dn\u011b zastoupen\u00fdch skupin p\u0159ikl\u00e1d\u00e1 v\u011bt\u0161\u00ed v\u00fdznam, aby se vzorek vyrovnal.&nbsp;<\/p>\n\n\n\n<h4>N\u00e1stroje pro monitorov\u00e1n\u00ed v re\u00e1ln\u00e9m \u010dase<\/h4>\n\n\n\n<p>N\u00e1stroje pro monitorov\u00e1n\u00ed v re\u00e1ln\u00e9m \u010dase umo\u017e\u0148uj\u00ed sledovat sb\u011br dat v pr\u016fb\u011bhu jejich sb\u011bru, co\u017e v\u00e1m d\u00e1v\u00e1 mo\u017enost odhalit zkreslen\u00ed hned, jak se objev\u00ed. Pokud nap\u0159\u00edklad prov\u00e1d\u00edte rozs\u00e1hlou studii, kter\u00e1 shroma\u017e\u010fuje data po dobu n\u011bkolika m\u011bs\u00edc\u016f, monitorov\u00e1n\u00ed v re\u00e1ln\u00e9m \u010dase v\u00e1s m\u016f\u017ee upozornit, pokud jsou n\u011bkter\u00e9 skupiny nedostate\u010dn\u011b zastoupeny nebo pokud se data za\u010dnou vych\u00fdlit jedn\u00edm sm\u011brem.<\/p>\n\n\n\n<p>Pro zaji\u0161t\u011bn\u00ed spolehlivosti a p\u0159esnosti v\u00fdzkumu je z\u00e1sadn\u00ed \u0159e\u0161it zkreslen\u00ed zji\u0161t\u011bn\u00ed. Dodr\u017eov\u00e1n\u00edm praktick\u00fdch strategi\u00ed, jako je n\u00e1hodn\u00fd v\u00fdb\u011br vzork\u016f, zvy\u0161ov\u00e1n\u00ed rozmanitosti vzorku a pou\u017e\u00edv\u00e1n\u00ed kontroln\u00edch skupin, m\u016f\u017eete sn\u00ed\u017eit pravd\u011bpodobnost zkreslen\u00ed p\u0159i sb\u011bru dat.&nbsp;<\/p>\n\n\n\n<p>Z\u00e1v\u011brem lze \u0159\u00edci, \u017ee \u0159e\u0161en\u00ed zkreslen\u00ed p\u0159i zji\u0161\u0165ov\u00e1n\u00ed je z\u00e1sadn\u00ed pro zaji\u0161t\u011bn\u00ed p\u0159esnosti a spolehlivosti \u00fadaj\u016f, kter\u00e9 shroma\u017e\u010fujete a analyzujete. Zaveden\u00edm strategi\u00ed, jako je n\u00e1hodn\u00fd v\u00fdb\u011br vzork\u016f, zv\u00fd\u0161en\u00ed rozmanitosti vzorku, prov\u00e1d\u011bn\u00ed longitudin\u00e1ln\u00edch a pilotn\u00edch studi\u00ed a pou\u017e\u00edv\u00e1n\u00ed kontroln\u00edch skupin, m\u016f\u017eete pravd\u011bpodobnost zkreslen\u00ed ve sv\u00e9m v\u00fdzkumu v\u00fdrazn\u011b sn\u00ed\u017eit.&nbsp;<\/p>\n\n\n\n<p>Tyto metody spole\u010dn\u011b pom\u00e1haj\u00ed vytv\u00e1\u0159et p\u0159esn\u011bj\u0161\u00ed a reprezentativn\u011bj\u0161\u00ed zji\u0161t\u011bn\u00ed, \u010d\u00edm\u017e se zvy\u0161uje kvalita a validita v\u00fdsledk\u016f v\u00fdzkumu.<\/p>\n\n\n\n<p><strong>Souvisej\u00edc\u00ed \u010dl\u00e1nek:<\/strong>&nbsp; <a href=\"https:\/\/mindthegraph.com\/blog\/how-to-avoid-bias-in-research\/\"><strong>Jak se vyhnout p\u0159edpojatosti ve v\u00fdzkumu: Jak se orientovat ve v\u011bdeck\u00e9 objektivit\u011b<\/strong><\/a><\/p>\n\n\n\n<h2>V\u011bdeck\u00e9 obr\u00e1zky, grafick\u00e9 abstrakty a infografiky pro v\u00e1\u0161 v\u00fdzkum<\/h2>\n\n\n\n<p>Hled\u00e1te v\u011bdeck\u00e9 \u00fadaje, grafick\u00e9 abstrakty a infografiky na jednom m\u00edst\u011b? Tak tady to je! <a href=\"https:\/\/mindthegraph.com\/science-figures\/?utm_source=blog&amp;utm_medium=cta-final&amp;utm_campaign=conversion\">Mind the Graph<\/a> v\u00e1m p\u0159in\u00e1\u0161\u00ed sb\u00edrku vizu\u00e1ln\u00edch materi\u00e1l\u016f, kter\u00e9 jsou ide\u00e1ln\u00ed pro v\u00e1\u0161 v\u00fdzkum. V platform\u011b si m\u016f\u017eete vybrat z p\u0159edp\u0159ipraven\u00fdch grafik a p\u0159izp\u016fsobit si ji podle sv\u00fdch pot\u0159eb. M\u016f\u017eete si dokonce nechat pomoci od na\u0161ich n\u00e1vrh\u00e1\u0159\u016f a kur\u00e1tor\u016f a vytvo\u0159it si specifick\u00e9 abstrakty na z\u00e1klad\u011b t\u00e9matu va\u0161eho v\u00fdzkumu. Na co tedy \u010dekat? Zaregistrujte se na Mind the Graph hned te\u010f a z\u00edskejte ve sv\u00e9m v\u00fdzkumu eso.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Mind the Graph - Tv\u016frce v\u011bdeck\u00fdch infografik\" width=\"800\" height=\"450\" src=\"https:\/\/www.youtube.com\/embed\/tG-PmLzx6NA?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption class=\"wp-element-caption\">Prozkoumejte hloubku v\u011bdomost\u00ed a poznatk\u016f d\u00edky tomuto poutav\u00e9mu videu. \ud83c\udf1f<\/figcaption><\/figure>\n\n\n\n<div class=\"is-content-justification-center is-layout-flex wp-container-1 wp-block-buttons\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-background wp-element-button\" href=\"https:\/\/mindthegraph.com\/science-figures\/?utm_source=blog&amp;utm_medium=cta-final&amp;utm_campaign=conversion\" style=\"background-color:#7833ff\"><strong>Registrace do Mind the Graph<\/strong><\/a><\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>P\u0159e\u010dt\u011bte si o zkreslen\u00ed zji\u0161\u0165ov\u00e1n\u00ed, jeho p\u0159\u00ed\u010din\u00e1ch a praktick\u00fdch strategi\u00edch, jak zabr\u00e1nit zkreslen\u00ed \u00fadaj\u016f ve v\u00fdzkumu.<\/p>","protected":false},"author":33,"featured_media":55860,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[976,961],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Ascertainment Bias: How to Identify and Prevent It in Research - Mind the Graph Blog<\/title>\n<meta name=\"description\" content=\"Learn about ascertainment bias, its causes, and practical strategies to prevent data distortion in research.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mindthegraph.com\/blog\/cs\/ascertainment-bias\/\" \/>\n<meta property=\"og:locale\" content=\"cs_CZ\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ascertainment Bias: How to Identify and Prevent It in Research - Mind the Graph Blog\" \/>\n<meta property=\"og:description\" content=\"Learn about ascertainment bias, its causes, and practical strategies to prevent data distortion in research.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/mindthegraph.com\/blog\/cs\/ascertainment-bias\/\" \/>\n<meta property=\"og:site_name\" content=\"Mind the Graph Blog\" \/>\n<meta property=\"article:published_time\" content=\"2025-01-16T15:29:50+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-01-23T15:43:07+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/ascertainment_bias.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1124\" \/>\n\t<meta property=\"og:image:height\" content=\"613\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Sowjanya Pedada\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Sowjanya Pedada\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"13 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Ascertainment Bias: How to Identify and Prevent It in Research - Mind the Graph Blog","description":"Learn about ascertainment bias, its causes, and practical strategies to prevent data distortion in research.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/mindthegraph.com\/blog\/cs\/ascertainment-bias\/","og_locale":"cs_CZ","og_type":"article","og_title":"Ascertainment Bias: How to Identify and Prevent It in Research - Mind the Graph Blog","og_description":"Learn about ascertainment bias, its causes, and practical strategies to prevent data distortion in research.","og_url":"https:\/\/mindthegraph.com\/blog\/cs\/ascertainment-bias\/","og_site_name":"Mind the Graph Blog","article_published_time":"2025-01-16T15:29:50+00:00","article_modified_time":"2025-01-23T15:43:07+00:00","og_image":[{"width":1124,"height":613,"url":"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2025\/01\/ascertainment_bias.png","type":"image\/png"}],"author":"Sowjanya Pedada","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Sowjanya Pedada","Est. reading time":"13 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/mindthegraph.com\/blog\/ascertainment-bias\/","url":"https:\/\/mindthegraph.com\/blog\/ascertainment-bias\/","name":"Ascertainment Bias: How to Identify and Prevent It in Research - Mind the Graph Blog","isPartOf":{"@id":"https:\/\/mindthegraph.com\/blog\/#website"},"datePublished":"2025-01-16T15:29:50+00:00","dateModified":"2025-01-23T15:43:07+00:00","author":{"@id":"https:\/\/mindthegraph.com\/blog\/#\/schema\/person\/1809367ac22d998ef1780e61c942bd9e"},"description":"Learn about ascertainment bias, its causes, and practical strategies to prevent data distortion in research.","breadcrumb":{"@id":"https:\/\/mindthegraph.com\/blog\/ascertainment-bias\/#breadcrumb"},"inLanguage":"cs-CZ","potentialAction":[{"@type":"ReadAction","target":["https:\/\/mindthegraph.com\/blog\/ascertainment-bias\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/mindthegraph.com\/blog\/ascertainment-bias\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/mindthegraph.com\/blog\/"},{"@type":"ListItem","position":2,"name":"Ascertainment Bias: How to Identify and Prevent It in Research"}]},{"@type":"WebSite","@id":"https:\/\/mindthegraph.com\/blog\/#website","url":"https:\/\/mindthegraph.com\/blog\/","name":"Mind the Graph Blog","description":"Your science can be beautiful!","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/mindthegraph.com\/blog\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"cs-CZ"},{"@type":"Person","@id":"https:\/\/mindthegraph.com\/blog\/#\/schema\/person\/1809367ac22d998ef1780e61c942bd9e","name":"Sowjanya Pedada","image":{"@type":"ImageObject","inLanguage":"cs-CZ","@id":"https:\/\/mindthegraph.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/5498cb1111b92c813c76ae76ad5b1dd3?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/5498cb1111b92c813c76ae76ad5b1dd3?s=96&d=mm&r=g","caption":"Sowjanya Pedada"},"description":"Sowjanya is a passionate writer and an avid reader. She holds MBA in Agribusiness Management and now is working as a content writer. She loves to play with words and hopes to make a difference in the world through her writings. Apart from writing, she is interested in reading fiction novels and doing craftwork. She also loves to travel and explore different cuisines and spend time with her family and friends.","url":"https:\/\/mindthegraph.com\/blog\/cs\/author\/sowjanya\/"}]}},"_links":{"self":[{"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/posts\/55859"}],"collection":[{"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/users\/33"}],"replies":[{"embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/comments?post=55859"}],"version-history":[{"count":1,"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/posts\/55859\/revisions"}],"predecessor-version":[{"id":55863,"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/posts\/55859\/revisions\/55863"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/media\/55860"}],"wp:attachment":[{"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/media?parent=55859"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/categories?post=55859"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/cs\/wp-json\/wp\/v2\/tags?post=55859"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}