{"id":28012,"date":"2023-05-24T10:07:19","date_gmt":"2023-05-24T13:07:19","guid":{"rendered":"https:\/\/mindthegraph.com\/blog\/?p=28012"},"modified":"2023-05-24T10:07:21","modified_gmt":"2023-05-24T13:07:21","slug":"sampling-bias","status":"publish","type":"post","link":"https:\/\/mindthegraph.com\/blog\/sk\/vyberove-skreslenie\/","title":{"rendered":"Probl\u00e9m naz\u00fdvan\u00fd skreslenie v\u00fdberu vzorky"},"content":{"rendered":"<p>Bez oh\u013eadu na pou\u017eit\u00fa metodiku alebo sk\u00faman\u00fa discipl\u00ednu musia v\u00fdskumn\u00edci zabezpe\u010di\u0165, aby pou\u017e\u00edvali reprezentat\u00edvne vzorky, ktor\u00e9 odr\u00e1\u017eaj\u00fa charakteristiky sk\u00famanej popul\u00e1cie. V tomto \u010dl\u00e1nku sa budeme zaobera\u0165 konceptom v\u00fdberov\u00e9ho skreslenia, jeho r\u00f4znymi typmi a sp\u00f4sobmi uplat\u0148ovania a osved\u010den\u00fdmi postupmi na zmiernenie jeho \u00fa\u010dinkov.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u010co je to skreslenie v\u00fdberu vzorky?<\/h2>\n\n\n\n<p>Skreslenie v\u00fdberu vzorky sa vz\u0165ahuje na situ\u00e1ciu, ke\u010f s\u00fa niektor\u00ed jednotlivci alebo skupiny v popul\u00e1cii s v\u00e4\u010d\u0161ou pravdepodobnos\u0165ou zahrnut\u00ed do vzorky ako ostatn\u00ed, \u010do vedie k skresleniu alebo nereprezentat\u00edvnosti vzorky. M\u00f4\u017ee k tomu d\u00f4js\u0165 z r\u00f4znych d\u00f4vodov, ako s\u00fa napr\u00edklad nen\u00e1hodn\u00e9 met\u00f3dy v\u00fdberu vzorky, skreslenie vlastn\u00e9ho v\u00fdberu alebo skreslenie v\u00fdskumn\u00edka.<\/p>\n\n\n\n<p>In\u00fdmi slovami, skreslenie v\u00fdberu m\u00f4\u017ee naru\u0161i\u0165 platnos\u0165 a zov\u0161eobecnite\u013enos\u0165 v\u00fdsledkov v\u00fdskumu t\u00fdm, \u017ee vzorka je skreslen\u00e1 v prospech ur\u010dit\u00fdch charakterist\u00edk alebo h\u013ead\u00edsk, ktor\u00e9 nemusia by\u0165 reprezentat\u00edvne pre v\u00e4\u010d\u0161iu popul\u00e1ciu.&nbsp;<\/p>\n\n\n\n<p>V ide\u00e1lnom pr\u00edpade mus\u00edte v\u0161etk\u00fdch \u00fa\u010dastn\u00edkov prieskumu vybra\u0165 n\u00e1hodn\u00fdm sp\u00f4sobom. V praxi v\u0161ak m\u00f4\u017ee by\u0165 \u0165a\u017ek\u00e9 urobi\u0165 n\u00e1hodn\u00fd v\u00fdber \u00fa\u010dastn\u00edkov kv\u00f4li obmedzeniam, ako s\u00fa n\u00e1klady a dostupnos\u0165 respondentov. Aj ke\u010f nerob\u00edte n\u00e1hodn\u00fd v\u00fdber \u00fadajov, je nevyhnutn\u00e9 uvedomi\u0165 si potenci\u00e1lne skreslenia, ktor\u00e9 by sa mohli vyskytn\u00fa\u0165 vo va\u0161ich \u00fadajoch.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Medzi pr\u00edklady skreslenia v\u00fdberu vzorky patria:<\/h3>\n\n\n\n<ol>\n<li><strong>Predpojatos\u0165 dobrovo\u013en\u00edkov<\/strong>: \u00da\u010dastn\u00edci, ktor\u00ed sa dobrovo\u013ene z\u00fa\u010dastnia na \u0161t\u00fadii, m\u00f4\u017eu ma\u0165 in\u00e9 charakteristiky ako t\u00ed, ktor\u00ed sa dobrovo\u013ene nez\u00fa\u010dastnia, \u010do vedie k nereprezentat\u00edvnej vzorke.<\/li>\n\n\n\n<li><strong>Nen\u00e1hodn\u00fd v\u00fdber vzoriek<\/strong>: Ak v\u00fdskumn\u00edk vyber\u00e1 \u00fa\u010dastn\u00edkov len z ur\u010dit\u00fdch miest alebo len \u00fa\u010dastn\u00edkov s ur\u010dit\u00fdmi charakteristikami, m\u00f4\u017ee to vies\u0165 k neobjekt\u00edvnej vzorke.<\/li>\n\n\n\n<li><strong>Predsudok o pre\u017eit\u00ed<\/strong>: K tomu doch\u00e1dza vtedy, ke\u010f vzorka zah\u0155\u0148a len jednotlivcov, ktor\u00ed pre\u017eili alebo uspeli v ur\u010ditej situ\u00e1cii, pri\u010dom sa vynechaj\u00fa t\u00ed, ktor\u00ed nepre\u017eili alebo neuspeli.<\/li>\n\n\n\n<li><strong>V\u00fdhodn\u00fd odber vzoriek<\/strong>: Tento typ v\u00fdberu vzorky zah\u0155\u0148a v\u00fdber \u013eahko dostupn\u00fdch \u00fa\u010dastn\u00edkov, napr\u00edklad t\u00fdch, ktor\u00ed sa nach\u00e1dzaj\u00fa v bl\u00edzkosti, alebo t\u00fdch, ktor\u00ed odpovedaj\u00fa na online prieskum, \u010do nemus\u00ed reprezentova\u0165 v\u00e4\u010d\u0161iu popul\u00e1ciu.<\/li>\n\n\n\n<li><strong>Potvrdzuj\u00faca zaujatos\u0165<\/strong>: V\u00fdskumn\u00edci m\u00f4\u017eu nevedome alebo z\u00e1merne vybra\u0165 \u00fa\u010dastn\u00edkov, ktor\u00ed podporuj\u00fa ich hypot\u00e9zu alebo v\u00fdskumn\u00fa ot\u00e1zku, \u010do vedie k neobjekt\u00edvnym v\u00fdsledkom.<\/li>\n\n\n\n<li><strong>Hawthornov efekt<\/strong>: \u00da\u010dastn\u00edci m\u00f4\u017eu zmeni\u0165 svoje spr\u00e1vanie alebo odpovede, ke\u010f vedia, \u017ee s\u00fa sk\u00faman\u00ed alebo pozorovan\u00ed, \u010do vedie k nereprezentat\u00edvnym v\u00fdsledkom.<\/li>\n<\/ol>\n\n\n\n<p>&nbsp;Ak ste si vedom\u00ed t\u00fdchto skreslen\u00ed, m\u00f4\u017eete ich zoh\u013eadni\u0165 v anal\u00fdze, aby ste vykonali korekciu skreslenia a lep\u0161ie pochopili popul\u00e1ciu, ktor\u00fa va\u0161e \u00fadaje reprezentuj\u00fa.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Typy skreslenia v\u00fdberu vzorky<\/h2>\n\n\n\n<ul>\n<li><strong>Predpojatos\u0165 pri v\u00fdbere<\/strong>: nast\u00e1va vtedy, ke\u010f vzorka nie je reprezentat\u00edvna pre popul\u00e1ciu.<\/li>\n\n\n\n<li><strong>Skreslenie merania<\/strong>: nast\u00e1va vtedy, ke\u010f s\u00fa zozbieran\u00e9 \u00fadaje nepresn\u00e9 alebo ne\u00fapln\u00e9.<\/li>\n\n\n\n<li><strong>Predpojatos\u0165 pri pod\u00e1van\u00ed spr\u00e1v<\/strong>: nast\u00e1va vtedy, ke\u010f respondenti poskytn\u00fa nepresn\u00e9 alebo ne\u00fapln\u00e9 inform\u00e1cie.<\/li>\n\n\n\n<li><strong>Neodpovedanie na ot\u00e1zky<\/strong>: nast\u00e1va vtedy, ke\u010f niektor\u00ed \u010dlenovia popul\u00e1cie na prieskum neodpovedaj\u00fa, \u010do vedie k nereprezentat\u00edvnej vzorke.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Pr\u00ed\u010diny skreslenia v\u00fdberu vzorky<\/h2>\n\n\n\n<ol>\n<li><strong>V\u00fdhodn\u00fd odber vzoriek<\/strong>: v\u00fdber vzorky na z\u00e1klade v\u00fdhodnosti namiesto pou\u017eitia vedeckej met\u00f3dy.<\/li>\n\n\n\n<li><strong>Predpojatos\u0165 vlastn\u00e9ho v\u00fdberu<\/strong>: do prieskumu s\u00fa zahrnut\u00ed len t\u00ed, ktor\u00ed sa ho dobrovo\u013ene z\u00fa\u010dastnili, \u010do nemus\u00ed by\u0165 reprezentat\u00edvne pre cel\u00fa popul\u00e1ciu.<\/li>\n\n\n\n<li><strong>Skreslenie v\u00fdberov\u00e9ho r\u00e1mca<\/strong>: ke\u010f v\u00fdberov\u00fd s\u00fabor pou\u017eit\u00fd na v\u00fdber vzorky nie je reprezentat\u00edvny pre popul\u00e1ciu.<\/li>\n\n\n\n<li><strong>Predsudok o pre\u017eit\u00ed<\/strong>: ke\u010f sa na prieskume z\u00fa\u010dast\u0148uj\u00fa len niektor\u00ed \u010dlenovia popul\u00e1cie, \u010do vedie k nereprezentat\u00edvnej vzorke. Napr\u00edklad, ak v\u00fdskumn\u00edci robia prieskum len u \u017eij\u00facich \u013eud\u00ed, nemusia z\u00edska\u0165 inform\u00e1cie od \u013eud\u00ed, ktor\u00ed zomreli pred uskuto\u010dnen\u00edm \u0161t\u00fadie.<\/li>\n\n\n\n<li><strong>Skreslenie v\u00fdberu vzorky v d\u00f4sledku nedostato\u010dn\u00fdch znalost\u00ed<\/strong>: nerozpoznanie zdrojov variability, ktor\u00e9 m\u00f4\u017eu vies\u0165 k skreslen\u00fdm odhadom.<\/li>\n\n\n\n<li><strong>Skreslenie v\u00fdberu vzorky v d\u00f4sledku ch\u00fdb pri pod\u00e1van\u00ed vzorky<\/strong>: nepou\u017eitie vhodn\u00e9ho alebo dobre funguj\u00faceho r\u00e1mca v\u00fdberu vzorky alebo odmietnutie \u00fa\u010dasti na \u0161t\u00fadii ved\u00face k neobjekt\u00edvnemu v\u00fdberu vzorky.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Predpojatos\u0165 pri v\u00fdbere vzorky v klinick\u00fdch sk\u00fa\u0161kach<\/h2>\n\n\n\n<p>Klinick\u00e9 sk\u00fa\u0161ky sl\u00fa\u017eia na testovanie \u00fa\u010dinnosti novej lie\u010dby alebo liekov na konkr\u00e9tnej popul\u00e1cii. S\u00fa d\u00f4le\u017eitou s\u00fa\u010das\u0165ou procesu v\u00fdvoja liekov a ur\u010duj\u00fa, \u010di je lie\u010dba bezpe\u010dn\u00e1 a \u00fa\u010dinn\u00e1 pred jej uvo\u013enen\u00edm pre \u0161irok\u00fa verejnos\u0165. Klinick\u00e9 sk\u00fa\u0161ky s\u00fa v\u0161ak tie\u017e n\u00e1chyln\u00e9 na v\u00fdberov\u00e9 skreslenie.<\/p>\n\n\n\n<p>K v\u00fdberov\u00e9mu skresleniu doch\u00e1dza vtedy, ke\u010f vzorka pou\u017eit\u00e1 na \u0161t\u00fadiu nie je reprezentat\u00edvna pre popul\u00e1ciu, ktor\u00fa m\u00e1 reprezentova\u0165. V pr\u00edpade klinick\u00fdch \u0161t\u00fadi\u00ed sa v\u00fdberov\u00e9 skreslenie m\u00f4\u017ee vyskytn\u00fa\u0165, ke\u010f s\u00fa \u00fa\u010dastn\u00edci bu\u010f selekt\u00edvne vybran\u00ed na \u00fa\u010das\u0165, alebo s\u00fa vybran\u00ed sami.<\/p>\n\n\n\n<p>Povedzme, \u017ee farmaceutick\u00e1 spolo\u010dnos\u0165 vykon\u00e1va klinick\u00e9 sk\u00fa\u0161anie s cie\u013eom otestova\u0165 \u00fa\u010dinnos\u0165 nov\u00e9ho lieku proti rakovine. Rozhodne sa na \u0161t\u00fadiu z\u00edskava\u0165 \u00fa\u010dastn\u00edkov prostredn\u00edctvom inzer\u00e1tov v nemocniciach, na klinik\u00e1ch a v podporn\u00fdch skupin\u00e1ch pre lie\u010dbu rakoviny, ako aj prostredn\u00edctvom online \u017eiadost\u00ed. Vzorka, ktor\u00fa zhroma\u017e\u010fuj\u00fa, v\u0161ak m\u00f4\u017ee by\u0165 zaujat\u00e1 t\u00fdmi, ktor\u00ed s\u00fa viac motivovan\u00ed z\u00fa\u010dastni\u0165 sa na sk\u00fa\u0161ke alebo ktor\u00ed maj\u00fa ur\u010dit\u00fd typ rakoviny. To m\u00f4\u017ee s\u0165a\u017ei\u0165 zov\u0161eobecnenie v\u00fdsledkov \u0161t\u00fadie na v\u00e4\u010d\u0161iu popul\u00e1ciu.<\/p>\n\n\n\n<p>Aby sa minimalizovalo skreslenie v\u00fdberu v klinick\u00fdch \u0161t\u00fadi\u00e1ch, v\u00fdskumn\u00edci musia zavies\u0165 pr\u00edsne krit\u00e9ri\u00e1 zaradenia a vyl\u00fa\u010denia a postupy n\u00e1hodn\u00e9ho v\u00fdberu. T\u00fdm sa zabezpe\u010d\u00ed, \u017ee vzorka \u00fa\u010dastn\u00edkov vybran\u00fdch do \u0161t\u00fadie je reprezentat\u00edvna pre v\u00e4\u010d\u0161iu popul\u00e1ciu, \u010d\u00edm sa minimalizuje ak\u00e9ko\u013evek skreslenie zozbieran\u00fdch \u00fadajov.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Probl\u00e9my sp\u00f4soben\u00e9 skreslen\u00edm v\u00fdberu vzorky<\/h2>\n\n\n\n<p>V\u00fdberov\u00e9 skreslenie je problematick\u00e9, preto\u017ee je mo\u017en\u00e9, \u017ee \u0161tatistika vypo\u010d\u00edtan\u00e1 zo vzorky je systematicky chybn\u00e1. M\u00f4\u017ee to vies\u0165 k systematick\u00e9mu nadhodnoteniu alebo podhodnoteniu pr\u00edslu\u0161n\u00e9ho parametra v popul\u00e1cii. Vyskytuje sa v praxi, preto\u017ee prakticky nie je mo\u017en\u00e9 zabezpe\u010di\u0165 dokonal\u00fa n\u00e1hodnos\u0165 pri v\u00fdbere vzorky.<\/p>\n\n\n\n<p>Ak je miera skreslenia mal\u00e1, vzorku mo\u017eno pova\u017eova\u0165 za primeran\u00fa aproxim\u00e1ciu n\u00e1hodnej vzorky. Okrem toho, ak sa vzorka v\u00fdrazne nel\u00ed\u0161i v meranej veli\u010dine, potom m\u00f4\u017ee by\u0165 skreslen\u00e1 vzorka st\u00e1le primeran\u00fdm odhadom.<\/p>\n\n\n\n<p>Hoci niektor\u00ed jednotlivci m\u00f4\u017eu z\u00e1merne pou\u017ei\u0165 neobjekt\u00edvnu vzorku, aby dosiahli zav\u00e1dzaj\u00face v\u00fdsledky, \u010dastej\u0161ie je neobjekt\u00edvna vzorka len odrazom \u0165a\u017ekost\u00ed pri z\u00edskavan\u00ed skuto\u010dne reprezentat\u00edvnej vzorky alebo neznalosti neobjekt\u00edvnosti v ich procese merania alebo anal\u00fdzy.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Extrapol\u00e1cia: mimo rozsahu<\/h2>\n\n\n\n<p>V \u0161tatistike sa vyvodzovanie z\u00e1verov o nie\u010dom, \u010do presahuje rozsah \u00fadajov, naz\u00fdva extrapol\u00e1cia. Vyvodzovanie z\u00e1verov zo skreslenej vzorky je jednou z foriem extrapol\u00e1cie: ke\u010f\u017ee met\u00f3da v\u00fdberu vzorky systematicky vylu\u010duje ur\u010dit\u00e9 \u010dasti sk\u00famanej popul\u00e1cie, z\u00e1very sa vz\u0165ahuj\u00fa len na vybran\u00fa subpopul\u00e1ciu.<\/p>\n\n\n\n<p>K extrapol\u00e1cii doch\u00e1dza aj vtedy, ak sa napr\u00edklad z\u00e1ver zalo\u017een\u00fd na vzorke vysoko\u0161kol\u00e1kov aplikuje na star\u0161\u00edch dospel\u00fdch alebo na dospel\u00fdch, ktor\u00ed maj\u00fa len osemro\u010dn\u00e9 vzdelanie. Extrapol\u00e1cia je be\u017enou chybou pri uplat\u0148ovan\u00ed alebo interpret\u00e1cii \u0161tatistiky. Niekedy je extrapol\u00e1cia kv\u00f4li \u0165a\u017ekostiam alebo nemo\u017enosti z\u00edska\u0165 kvalitn\u00e9 \u00fadaje to najlep\u0161ie, \u010do m\u00f4\u017eeme urobi\u0165, ale v\u017edy ju treba bra\u0165 aspo\u0148 s rezervou - a \u010dasto s ve\u013ekou d\u00e1vkou neistoty<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Z vedy do pseudovedy<\/h2>\n\n\n\n<p><a href=\"https:\/\/en.wikipedia.org\/wiki\/Sampling_bias\">Ako sa uv\u00e1dza vo Wikip\u00e9dii<\/a>, pr\u00edkladom toho, ako m\u00f4\u017ee existova\u0165 neznalos\u0165 zaujatosti, je roz\u0161\u00edren\u00e9 pou\u017e\u00edvanie pomeru (tzv. fold change) ako miery rozdielu v biol\u00f3gii. Ke\u010f\u017ee je \u013eah\u0161ie dosiahnu\u0165 ve\u013ek\u00fd pomer pri dvoch mal\u00fdch \u010d\u00edslach s dan\u00fdm rozdielom a relat\u00edvne \u0165a\u017e\u0161ie dosiahnu\u0165 ve\u013ek\u00fd pomer pri dvoch ve\u013ek\u00fdch \u010d\u00edslach s v\u00e4\u010d\u0161\u00edm rozdielom, pri porovn\u00e1van\u00ed relat\u00edvne ve\u013ek\u00fdch \u010d\u00edseln\u00fdch meran\u00ed sa m\u00f4\u017eu prehliadnu\u0165 ve\u013ek\u00e9 v\u00fdznamn\u00e9 rozdiely.&nbsp;<\/p>\n\n\n\n<p>Niektor\u00ed to naz\u00fdvaj\u00fa \"demarka\u010dn\u00fdm skreslen\u00edm\", preto\u017ee pou\u017eitie pomeru (delenia) namiesto rozdielu (od\u010d\u00edtania) pos\u00fava v\u00fdsledky anal\u00fdzy z vedy do pseudovedy.<\/p>\n\n\n\n<p>Niektor\u00e9 vzorky pou\u017e\u00edvaj\u00fa skreslen\u00fd \u0161tatistick\u00fd dizajn, ktor\u00fd v\u0161ak umo\u017e\u0148uje odhad parametrov. Napr\u00edklad N\u00e1rodn\u00e9 centrum pre zdravotn\u00fa \u0161tatistiku USA v mnoh\u00fdch svojich celo\u0161t\u00e1tnych prieskumoch z\u00e1merne vyber\u00e1 nadmern\u00e9 vzorky men\u0161inov\u00e9ho obyvate\u013estva, aby dosiahlo dostato\u010dn\u00fa presnos\u0165 odhadov v r\u00e1mci t\u00fdchto skup\u00edn.<\/p>\n\n\n\n<p>Tieto prieskumy si vy\u017eaduj\u00fa pou\u017eitie v\u00e1\u017een\u00fdch vzoriek, aby sa dosiahli spr\u00e1vne odhady vo v\u0161etk\u00fdch etnick\u00fdch skupin\u00e1ch. Ak s\u00fa splnen\u00e9 ur\u010dit\u00e9 podmienky (najm\u00e4 ak s\u00fa v\u00e1hy vypo\u010d\u00edtan\u00e9 a pou\u017eit\u00e9 spr\u00e1vne), tieto vzorky umo\u017e\u0148uj\u00fa presn\u00fd odhad parametrov popul\u00e1cie.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Osved\u010den\u00e9 postupy na zmiernenie skreslenia v\u00fdberu vzorky<\/h2>\n\n\n\n<p>Je ve\u013emi d\u00f4le\u017eit\u00e9 zvoli\u0165 vhodn\u00fa met\u00f3du v\u00fdberu vzorky, aby v\u00fdsledn\u00e9 \u00fadaje presne odr\u00e1\u017eali sk\u00faman\u00fa popul\u00e1ciu.<\/p>\n\n\n\n<ol>\n<li><strong>Techniky n\u00e1hodn\u00e9ho v\u00fdberu vzoriek<\/strong>: Pou\u017eitie techn\u00edk n\u00e1hodn\u00e9ho v\u00fdberu vzorky zvy\u0161uje pravdepodobnos\u0165, \u017ee vzorka je reprezentat\u00edvna pre popul\u00e1ciu. T\u00e1to technika pom\u00e1ha zabezpe\u010di\u0165, aby vzorka bola \u010do najreprezentat\u00edvnej\u0161ia pre dan\u00fa popul\u00e1ciu, a teda aby bola menej pravdepodobn\u00e1 jej neobjekt\u00edvnos\u0165.<\/li>\n\n\n\n<li><strong>V\u00fdpo\u010det ve\u013ekosti vzorky<\/strong>: V\u00fdpo\u010det ve\u013ekosti vzorky by sa mal vykona\u0165 tak, aby bola k dispoz\u00edcii primeran\u00e1 sila na testovanie \u0161tatisticky v\u00fdznamn\u00fdch hypot\u00e9z. \u010c\u00edm v\u00e4\u010d\u0161ia je ve\u013ekos\u0165 vzorky, t\u00fdm lep\u0161ie je zast\u00fapen\u00e1 popul\u00e1cia.<\/li>\n\n\n\n<li><strong>Anal\u00fdza trendov<\/strong>: H\u013eadanie alternat\u00edvnych zdrojov \u00fadajov a anal\u00fdza v\u0161etk\u00fdch pozorovan\u00fdch trendov v \u00fadajoch, ktor\u00e9 m\u00f4\u017eu by\u0165 nevybran\u00e9.<\/li>\n\n\n\n<li><strong>Kontrola zaujatosti<\/strong>: V\u00fdskyt zaujatosti by sa mal monitorova\u0165 s cie\u013eom identifikova\u0165 systematick\u00e9 vylu\u010dovanie alebo nadmern\u00e9 zahrnutie konkr\u00e9tnych \u00fadajov.<\/li>\n<\/ol>\n\n\n\n<p><strong>Pozor na vzorky<\/strong><\/p>\n\n\n\n<p>Skreslenie v\u00fdberu vzorky je v\u00fdznamn\u00fdm faktorom pri vykon\u00e1van\u00ed v\u00fdskumu. Bez oh\u013eadu na pou\u017eit\u00fa metodiku alebo sk\u00faman\u00fa discipl\u00ednu musia v\u00fdskumn\u00edci zabezpe\u010di\u0165, aby pou\u017e\u00edvali reprezentat\u00edvne vzorky, ktor\u00e9 odr\u00e1\u017eaj\u00fa charakteristiky sk\u00famanej popul\u00e1cie.<\/p>\n\n\n\n<p>Pri tvorbe v\u00fdskumn\u00fdch \u0161t\u00fadi\u00ed je nevyhnutn\u00e9 venova\u0165 ve\u013ek\u00fa pozornos\u0165 procesu v\u00fdberu vzorky, ako aj metodike pou\u017eitej na zber \u00fadajov zo vzorky. Mali by sa pou\u017e\u00edva\u0165 osved\u010den\u00e9 postupy, ako s\u00fa techniky n\u00e1hodn\u00e9ho v\u00fdberu vzoriek, v\u00fdpo\u010det ve\u013ekosti vzorky, anal\u00fdza trendov a kontrola zaujatosti, aby sa zabezpe\u010dilo, \u017ee v\u00fdsledky v\u00fdskumu bud\u00fa platn\u00e9 a spo\u013eahliv\u00e9, \u010d\u00edm sa zv\u00fd\u0161i pravdepodobnos\u0165, \u017ee ovplyvnia politiku a prax.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">P\u00fatav\u00e9 vedeck\u00e9 infografiky za p\u00e1r min\u00fat<\/h2>\n\n\n\n<p><a href=\"http:\/\/mindthegraph.com\/\">Mind the Graph<\/a> je v\u00fdkonn\u00fd online n\u00e1stroj pre vedcov, ktor\u00ed potrebuj\u00fa vytv\u00e1ra\u0165 vysokokvalitn\u00fa vedeck\u00fa grafiku a ilustr\u00e1cie. Platforma je u\u017e\u00edvate\u013esky pr\u00edvetiv\u00e1 a pr\u00edstupn\u00e1 vedcom s r\u00f4znou \u00farov\u0148ou technick\u00fdch znalost\u00ed, tak\u017ee je ide\u00e1lnym rie\u0161en\u00edm pre v\u00fdskumn\u00edkov, ktor\u00ed potrebuj\u00fa vytv\u00e1ra\u0165 grafiku pre svoje publik\u00e1cie, prezent\u00e1cie a in\u00e9 vedeck\u00e9 komunika\u010dn\u00e9 materi\u00e1ly.<\/p>\n\n\n\n<p>\u010ci u\u017e ste v\u00fdskumn\u00edk v oblasti pr\u00edrodn\u00fdch, fyzik\u00e1lnych alebo technick\u00fdch vied, Mind the Graph pon\u00faka \u0161irok\u00fa \u0161k\u00e1lu zdrojov, ktor\u00e9 v\u00e1m pom\u00f4\u017eu zrozumite\u013ene a vizu\u00e1lne presved\u010divo komunikova\u0165 v\u00fdsledky v\u00e1\u0161ho v\u00fdskumu.<\/p>\n\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"600\" height=\"338\" src=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2022\/10\/r3qiu0qenda-3.gif\" alt=\"\" class=\"wp-image-25130\"\/><\/figure><\/div>\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"is-layout-flex wp-block-buttons\">\n<div class=\"wp-block-button aligncenter\"><a class=\"wp-block-button__link has-background wp-element-button\" href=\"https:\/\/mindthegraph.com\/app\/offer-trial\" style=\"border-radius:50px;background-color:#dc1866\" target=\"_blank\" rel=\"noreferrer noopener\">Za\u010dnite vytv\u00e1ra\u0165 infografiky zadarmo<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:44px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>","protected":false},"excerpt":{"rendered":"<p>Skreslenie v\u00fdberu vzorky je d\u00f4le\u017eit\u00fdm faktorom pri v\u00fdskume v odboroch, ako je \u0161tatistika, soci\u00e1lne vedy a epidemiol\u00f3gia. <\/p>","protected":false},"author":38,"featured_media":28013,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[959,28],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.9 - 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