{"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\/lv\/ascertainment-bias\/","title":{"rendered":"P\u0101rliecin\u0101t\u012bbas neobjektivit\u0101te: k\u0101 to identific\u0113t un nov\u0113rst p\u0113tniec\u012bb\u0101"},"content":{"rendered":"<p>P\u0101rliecin\u0101t\u012bbas novirze ir bie\u017ei sastopama probl\u0113ma p\u0113tniec\u012bb\u0101, kas rodas tad, ja sav\u0101ktie dati prec\u012bzi neatspogu\u013co visu situ\u0101ciju. Lai uzlabotu datu ticam\u012bbu un nodro\u0161in\u0101tu prec\u012bzus p\u0113t\u012bjumu rezult\u0101tus, ir \u013coti svar\u012bgi izprast noskaidro\u0161anas novirzi. Lai gan da\u017ek\u0101rt t\u0101 izr\u0101d\u0101s noder\u012bga, t\u0101 nav vienm\u0113r.&nbsp;<\/p>\n\n\n\n<p>P\u0101rliecin\u0101t\u012bbas neobjektivit\u0101te rodas tad, ja sav\u0101ktie dati patiesi neatspogu\u013co visu situ\u0101ciju, jo ir liel\u0101ka varb\u016bt\u012bba, ka tiks sav\u0101kti noteikta veida dati nek\u0101 citi. Tas var izkrop\u013cot rezult\u0101tus, radot izkrop\u013cotu izpratni par to, kas paties\u012bb\u0101 notiek.<\/p>\n\n\n\n<p>Tas var \u0161\u0137ist mulsino\u0161i, ta\u010du izpratne par noskaidro\u0161anas novirzi pal\u012bdz jums b\u016bt kritisk\u0101kiem pret datiem, ar kuriem str\u0101d\u0101jat, t\u0101d\u0113j\u0101di padarot savus rezult\u0101tus ticam\u0101kus. \u0160aj\u0101 rakst\u0101 tiks padzi\u013cin\u0101ti izp\u0113t\u012bta \u0161\u012b novirze un izskaidrots viss par to. T\u0101p\u0113c bez kav\u0113\u0161an\u0101s s\u0101ksim!<\/p>\n\n\n\n<h2>P\u0101rliecin\u0101t\u012bbas neobjektivit\u0101tes izpratne p\u0113tniec\u012bb\u0101<\/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=\"Tuvupl\u0101ns rok\u0101m, kas raksta uz kl\u0113pjdatora, ar za\u013cu podi\u0146augu uz balta galda t\u012br\u0101 un minim\u0101lisma stil\u0101 iek\u0101rtot\u0101 darba viet\u0101.\" 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 no <a href=\"https:\/\/unsplash.com\/pt-br\/@nordwood?utm_content=creditCopyText&#038;utm_medium=referral&#038;utm_source=unsplash\">NordWood t\u0113mas<\/a> uz <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>Konstat\u0113\u0161anas neobjektivit\u0101te rodas, ja datu v\u0101k\u0161anas metod\u0113s priorit\u0101te tiek pie\u0161\u0137irta noteiktai inform\u0101cijai, kas noved pie izkrop\u013cotiem un nepiln\u012bgiem secin\u0101jumiem. Apzinoties, k\u0101 noskaidro\u0161anas novirze ietekm\u0113 j\u016bsu p\u0113t\u012bjumu, j\u016bs varat veikt pas\u0101kumus, lai samazin\u0101tu t\u0101s ietekmi un uzlabotu secin\u0101jumu ticam\u012bbu. T\u0101 notiek tad, ja ir liel\u0101ka varb\u016bt\u012bba, ka k\u0101da inform\u0101cija tiks apkopota, bet citi svar\u012bgi dati netiks apkopoti.&nbsp;<\/p>\n\n\n\n<p>Rezult\u0101t\u0101 j\u016bs varat izdar\u012bt secin\u0101jumus, kas neatbilst realit\u0101tei. Lai p\u0101rliecin\u0101tos, ka j\u016bsu secin\u0101jumi vai nov\u0113rojumi ir prec\u012bzi un ticami, ir svar\u012bgi izprast \u0161o neobjektivit\u0101ti.<\/p>\n\n\n\n<p>Vienk\u0101r\u0161\u0101k sakot, noskaidro\u0161anas neobjektivit\u0101te noz\u012bm\u0113, ka tas, ko j\u016bs apl\u016bkojat, nesniedz jums piln\u012bgu inform\u0101ciju. Iedom\u0101jieties, ka j\u016bs p\u0113t\u0101t to cilv\u0113ku skaitu, kuri valk\u0101 brilles, aptauj\u0101jot optometristu kabinetu.&nbsp;<\/p>\n\n\n\n<p>Jums ir liel\u0101ka iesp\u0113ja, ka tur sastapsiet cilv\u0113kus, kuriem nepiecie\u0161ama redzes korekcija, t\u0101p\u0113c j\u016bsu dati b\u016bs izkrop\u013coti, jo j\u016bs ne\u0146emat v\u0113r\u0101 cilv\u0113kus, kuri neapmekl\u0113 optometristu. Tas ir noskaidro\u0161anas novirzes piem\u0113rs.<\/p>\n\n\n\n<p>\u0160\u0101da neobjektivit\u0101te var rasties daudz\u0101s jom\u0101s, piem\u0113ram, vesel\u012bbas apr\u016bp\u0113, p\u0113tniec\u012bb\u0101 un pat ikdienas l\u0113mumu pie\u0146em\u0161an\u0101. Ja koncentr\u0113jaties tikai uz noteikta veida datiem vai inform\u0101ciju, j\u016bs varat nepaman\u012bt citus b\u016btiskus faktorus.&nbsp;<\/p>\n\n\n\n<p>Piem\u0113ram, k\u0101das slim\u012bbas p\u0113t\u012bjums var b\u016bt neobjekt\u012bvs, ja slimn\u012bc\u0101s nov\u0113ro tikai smag\u0101kos slim\u012bbas gad\u012bjumus, ne\u0146emot v\u0113r\u0101 viegl\u0101kus gad\u012bjumus, kas netiek atkl\u0101ti. Rezult\u0101t\u0101 slim\u012bba var \u0161\u0137ist smag\u0101ka vai izplat\u012bt\u0101ka, nek\u0101 t\u0101 ir paties\u012bb\u0101.<\/p>\n\n\n\n<h2>Bie\u017e\u0101kie noskaidro\u0161anas neobjektivit\u0101tes c\u0113lo\u0146i<\/h2>\n\n\n\n<p>Konstat\u0113\u0161anas neobjektivit\u0101tes c\u0113lo\u0146i ir da\u017e\u0101di - no selekt\u012bvas izlases l\u012bdz zi\u0146o\u0161anas neobjektivit\u0101tei, un katrs no tiem sav\u0101d\u0101 veid\u0101 veicina datu izkrop\u013co\u0161anu. Turpm\u0101k ir min\u0113ti da\u017ei no izplat\u012bt\u0101kajiem iemesliem, kas izraisa \u0161o novirzi:<\/p>\n\n\n\n<h3>Selekt\u012bv\u0101 paraugu \u0146em\u0161ana<\/h3>\n\n\n\n<p>Izv\u0113loties p\u0113t\u012bjumam tikai noteiktu cilv\u0113ku grupu vai datus, past\u0101v risks, ka tiks izsl\u0113gta cita svar\u012bga inform\u0101cija. Piem\u0113ram, ja aptauj\u0101 tiek iek\u013cautas tikai to cilv\u0113ku atbildes, kuri lieto konkr\u0113tu produktu, t\u0101 neatspogu\u013cos to cilv\u0113ku viedokli, kuri to nelieto. Tas noved pie neobjekt\u012bva secin\u0101juma, jo tie, kas nav lietot\u0101ji, netiek iesaist\u012bti datu v\u0101k\u0161anas proces\u0101.<\/p>\n\n\n\n<h2>Atkl\u0101\u0161anas metodes<\/h2>\n\n\n\n<p>Ar\u012b datu v\u0101k\u0161an\u0101 izmantotie r\u012bki vai metodes var rad\u012bt noskaidro\u0161anas novirzi. Piem\u0113ram, ja j\u016bs p\u0113t\u0101t k\u0101du medic\u012bnisku st\u0101vokli, bet izmantojat tikai testus, kas atkl\u0101j smagus simptomus, j\u016bs nepaman\u012bsiet gad\u012bjumus, kad simptomi ir viegli vai nav atkl\u0101ti. Tas izkrop\u013co rezult\u0101tus, padarot st\u0101vokli nopietn\u0101ku vai izplat\u012bt\u0101ku, nek\u0101 tas ir paties\u012bb\u0101.<\/p>\n\n\n\n<h2>P\u0113t\u012bjuma norises vieta<\/h2>\n\n\n\n<p>Da\u017ek\u0101rt p\u0113t\u012bjuma veik\u0161anas vieta var rad\u012bt neobjektivit\u0101ti. Piem\u0113ram, ja j\u016bs p\u0113t\u0101t sabiedr\u012bbas uzved\u012bbu, bet nov\u0113rojat cilv\u0113kus tikai noslogot\u0101 pils\u0113tas rajon\u0101, j\u016bsu dati neatspogu\u013cos cilv\u0113ku uzved\u012bbu klus\u0101k\u0101, lauku vid\u0113. Tas rada nepiln\u012bgu priek\u0161statu par visp\u0101r\u0113jo uzved\u012bbu, ko m\u0113\u0123in\u0101t izprast.<\/p>\n\n\n\n<h2>Zi\u0146o\u0161anas neobjektivit\u0101te<\/h2>\n\n\n\n<p>Cilv\u0113ki m\u0113dz zi\u0146ot vai dal\u012bties ar inform\u0101ciju, kas \u0161\u0137iet svar\u012bg\u0101ka vai steidzam\u0101ka. Medic\u012bnas p\u0113t\u012bjum\u0101 pacienti ar smagiem simptomiem, iesp\u0113jams, bie\u017e\u0101k v\u0113rs\u012bsies p\u0113c pal\u012bdz\u012bbas, bet pacienti ar viegliem simptomiem, iesp\u0113jams, pat nev\u0113rs\u012bsies pie \u0101rsta. Tas rada datu neobjektivit\u0101ti, jo p\u0101r\u0101k liela uzman\u012bba tiek piev\u0113rsta smagajiem gad\u012bjumiem un netiek \u0146emti v\u0113r\u0101 vieglie.<\/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;Mind the Graph rekl\u0101mas baneris, kur\u0101 teikts: &quot;Ar Mind the Graph bez piep\u016bles radiet zin\u0101tniskas ilustr\u0101cijas,&quot; uzsverot platformas lieto\u0161anas \u0113rtumu.&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\">Bez piep\u016bles veidojiet zin\u0101tniskas ilustr\u0101cijas, izmantojot <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>Bie\u017e\u0101k sastopam\u0101s situ\u0101cijas, kur\u0101s var rasties aizspriedumi<\/h2>\n\n\n\n<p>P\u0101rliecin\u0101t\u012bbas novirze var rasties da\u017e\u0101d\u0101s ikdienas situ\u0101cij\u0101s un p\u0113t\u012bjumos:<\/p>\n\n\n\n<h3>Vesel\u012bbas apr\u016bpes studijas<\/h3>\n\n\n\n<p>Ja p\u0113t\u012bjum\u0101 iek\u013cauj tikai to pacientu datus, kuri apmekl\u0113 slimn\u012bcu, tas var p\u0101rv\u0113rt\u0113t slim\u012bbas smaguma pak\u0101pi vai izplat\u012bbu, jo taj\u0101 netiek \u0146emti v\u0113r\u0101 pacienti ar viegliem simptomiem, kuri nemekl\u0113 \u0101rst\u0113\u0161anu.<\/p>\n\n\n\n<h3>Aptaujas un aptaujas<\/h3>\n\n\n\n<p>Iedom\u0101jieties, ka veicat aptauju, lai noskaidrotu cilv\u0113ku viedokli par k\u0101du produktu, bet aptauj\u0101jat tikai eso\u0161os klientus. Atsauksmes, visticam\u0101k, b\u016bs pozit\u012bvas, bet j\u016bs neesat uzzin\u0101jis to cilv\u0113ku viedok\u013cus, kuri produktu neizmanto. Tas var rad\u012bt neobjekt\u012bvu priek\u0161statu par to, k\u0101 produktu uztver sabiedr\u012bba kopum\u0101.<\/p>\n\n\n\n<h3>Nov\u0113rojuma p\u0113t\u012bjumi<\/h3>\n\n\n\n<p>Ja nov\u0113rojat dz\u012bvnieku uzved\u012bbu, bet p\u0113t\u0101t tikai zoolo\u0123iskaj\u0101 d\u0101rz\u0101 eso\u0161os dz\u012bvniekus, j\u016bsu dati neatspogu\u013cos to, k\u0101 \u0161ie dz\u012bvnieki uzvedas savva\u013c\u0101. Ierobe\u017eot\u0101 zoolo\u0123isk\u0101 d\u0101rza vide var izrais\u012bt cit\u0101du uzved\u012bbu nek\u0101 t\u0101, kas nov\u0113rota to dabiskaj\u0101 vid\u0113.<\/p>\n\n\n\n<p>Atz\u012bstot un izprotot \u0161os c\u0113lo\u0146us un piem\u0113rus, j\u016bs varat veikt pas\u0101kumus, lai nodro\u0161in\u0101tu, ka j\u016bsu datu v\u0101k\u0161ana un anal\u012bze ir prec\u012bz\u0101ka. Tas pal\u012bdz\u0113s jums izvair\u012bties no maldino\u0161u secin\u0101jumu izdar\u012b\u0161anas un \u013caus lab\u0101k izprast re\u0101lo situ\u0101ciju.<\/p>\n\n\n\n<h2>K\u0101 identific\u0113t neprecizit\u0101ti datos<\/h2>\n\n\n\n<p>Nov\u0113rt\u0113\u0161anas neobjektivit\u0101tes atpaz\u012b\u0161ana ietver datu avotu vai meto\u017eu identific\u0113\u0161anu, kas var nesam\u0113r\u012bgi veicin\u0101t noteiktus rezult\u0101tus sal\u012bdzin\u0101jum\u0101 ar citiem. Sp\u0113ja savlaic\u012bgi paman\u012bt ieg\u016b\u0161anas neobjektivit\u0101ti \u013cauj p\u0113tniekiem piel\u0101got savas metodes un nodro\u0161in\u0101t prec\u012bz\u0101kus rezult\u0101tus.<\/p>\n\n\n\n<p>\u0160\u012b neobjektivit\u0101te bie\u017ei vien sl\u0113pjas ac\u012bm redzam\u0101 veid\u0101, ietekm\u0113jot secin\u0101jumus un l\u0113mumus, bet nav uzreiz paman\u0101ma. Iem\u0101coties to paman\u012bt, j\u016bs varat uzlabot sava p\u0113t\u012bjuma precizit\u0101ti un izvair\u012bties no maldino\u0161u pie\u0146\u0113mumu izdar\u012b\u0161anas.<\/p>\n\n\n\n<h3>Paz\u012bmes, kas j\u0101mekl\u0113<\/h3>\n\n\n\n<p>Ir vair\u0101ki r\u0101d\u012bt\u0101ji, kas var pal\u012bdz\u0113t noteikt datu ieg\u016b\u0161anas novirzi. \u0160o paz\u012bmju apzin\u0101\u0161an\u0101s \u013caus jums r\u012bkoties un piel\u0101got datu v\u0101k\u0161anas vai anal\u012bzes metodes, lai samazin\u0101tu to ietekmi.<\/p>\n\n\n\n<h4>Selekt\u012bvie datu avoti<\/h4>\n\n\n\n<p>Viena no skaidr\u0101kaj\u0101m noskaidro\u0161anas neobjektivit\u0101tes paz\u012bm\u0113m ir tad, ja dati ir ieg\u016bti no ierobe\u017eota vai selekt\u012bva avota.&nbsp;<\/p>\n\n\n\n<h4>Tr\u016bksto\u0161ie dati<\/h4>\n\n\n\n<p>V\u0113l viens r\u0101d\u012bt\u0101js, kas liecina par noskaidro\u0161anas neobjektivit\u0101ti, ir tr\u016bksto\u0161i vai nepiln\u012bgi dati, jo \u012bpa\u0161i tad, ja da\u017eas grupas vai rezult\u0101ti ir nepietiekami p\u0101rst\u0101v\u0113ti.&nbsp;<\/p>\n\n\n\n<h4>P\u0101r\u0101k liela noteiktu grupu p\u0101rst\u0101v\u012bba<\/h4>\n\n\n\n<p>Neprecizit\u0101te var rasties ar\u012b tad, ja datu v\u0101k\u0161an\u0101 viena grupa ir p\u0101r\u0101k pla\u0161i p\u0101rst\u0101v\u0113ta. Pie\u0146emsim, ka j\u016bs p\u0113t\u0101t darba paradumus biroj\u0101 un koncentr\u0113jaties galvenok\u0101rt uz darbiniekiem, kas str\u0101d\u0101 ar augstu darba ra\u017e\u012bgumu. J\u016bsu sav\u0101ktie dati, visticam\u0101k, liecin\u0101tu, ka garas darba stundas un virsstundas veicina pan\u0101kumus. Tom\u0113r j\u016bs ignor\u0113jat citus darbiniekus, kuriem var\u0113tu b\u016bt at\u0161\u0137ir\u012bgi darba paradumi, un tas var\u0113tu novest pie neprec\u012bziem secin\u0101jumiem par to, kas patie\u0161\u0101m veicina pan\u0101kumus darbaviet\u0101.<\/p>\n\n\n\n<h4>Nesaska\u0146oti rezult\u0101ti da\u017e\u0101dos p\u0113t\u012bjumos<\/h4>\n\n\n\n<p>Ja nov\u0113rojat, ka j\u016bsu p\u0113t\u012bjuma rezult\u0101ti iev\u0113rojami at\u0161\u0137iras no citiem p\u0113t\u012bjumiem par to pa\u0161u t\u0113mu, tas var liecin\u0101t par to, ka ir nov\u0113rota noskaidro\u0161anas novirze.<\/p>\n\n\n\n<p>&nbsp;<strong>Lasiet ar\u012b: <\/strong><a href=\"https:\/\/mindthegraph.com\/blog\/publication-bias\/\"><strong>Publik\u0101ciju neobjektivit\u0101te: viss, kas jums j\u0101zina<\/strong><\/a><\/p>\n\n\n\n<h2>Noskaidro\u0161anas neobjektivit\u0101tes ietekme<\/h2>\n\n\n\n<p>P\u0101rliecin\u0101t\u012bbas novirze var b\u016btiski ietekm\u0113t p\u0113t\u012bjumu, l\u0113mumu pie\u0146em\u0161anas un politikas rezult\u0101tus. Izprotot, k\u0101 \u0161\u012b novirze ietekm\u0113 rezult\u0101tus, j\u016bs varat lab\u0101k nov\u0113rt\u0113t, cik svar\u012bgi ir to nov\u0113rst jau datu v\u0101k\u0161anas vai anal\u012bzes procesa s\u0101kum\u0101.<\/p>\n\n\n\n<h3>K\u0101 neobjektivit\u0101te ietekm\u0113 p\u0113t\u012bjumu rezult\u0101tus<\/h3>\n\n\n\n<h4>Izkrop\u013coti secin\u0101jumi<\/h4>\n\n\n\n<p>Visredzam\u0101k\u0101 noskaidro\u0161anas novirzes ietekme ir t\u0101, ka t\u0101 noved pie izkrop\u013cotiem secin\u0101jumiem. Ja da\u017ei datu punkti ir p\u0101rst\u0101v\u0113ti p\u0101rm\u0113r\u012bgi vai nepietiekami, ieg\u016btie rezult\u0101ti prec\u012bzi neatspogu\u013co realit\u0101ti.&nbsp;<\/p>\n\n\n\n<h4>Neprec\u012bzas prognozes<\/h4>\n\n\n\n<p>Ja p\u0113t\u012bjums ir neobjekt\u012bvs, ar\u012b prognozes, kas balst\u012btas uz \u0161o p\u0113t\u012bjumu, b\u016bs neprec\u012bzas. T\u0101d\u0101s jom\u0101s k\u0101 sabiedr\u012bbas vesel\u012bba neobjekt\u012bvi dati var novest pie k\u013c\u016bdain\u0101m prognoz\u0113m par slim\u012bbu izplat\u012bbu, \u0101rst\u0113\u0161anas efektivit\u0101ti vai sabiedr\u012bbas vesel\u012bbas interven\u010du ietekmi.<\/p>\n\n\n\n<h4>Neder\u012bgi visp\u0101rin\u0101jumi<\/h4>\n\n\n\n<p>Viens no liel\u0101kajiem ieguvumu noskaidro\u0161anas neobjektivit\u0101tes draudiem ir tas, ka t\u0101 var novest pie neder\u012bgiem visp\u0101rin\u0101jumiem. Jums var rasties k\u0101rdin\u0101jums sava p\u0113t\u012bjuma rezult\u0101tus attiecin\u0101t uz pla\u0161\u0101ku popul\u0101ciju, bet, ja j\u016bsu izlase ir bijusi neobjekt\u012bva, j\u016bsu secin\u0101jumi neb\u016bs pamatoti. Tas var b\u016bt \u012bpa\u0161i kait\u012bgi t\u0101d\u0101s jom\u0101s k\u0101 soci\u0101l\u0101s zin\u0101tnes vai izgl\u012bt\u012bba, kur p\u0113t\u012bjumu rezult\u0101tus bie\u017ei izmanto, lai izstr\u0101d\u0101tu politiku vai intervences pas\u0101kumus.<\/p>\n\n\n\n<h3>Iesp\u0113jam\u0101s sekas da\u017e\u0101d\u0101s jom\u0101s<\/h3>\n\n\n\n<p>P\u0101rliecin\u0101t\u012bbas novirzei var b\u016bt t\u0101lejo\u0161as sekas atkar\u012bb\u0101 no studiju vai darba jomas. Turpm\u0101k ir sniegti da\u017ei piem\u0113ri, k\u0101 \u0161\u012b neobjektivit\u0101te var ietekm\u0113t da\u017e\u0101das jomas:<\/p>\n\n\n\n<h4>Vesel\u012bbas apr\u016bpe<\/h4>\n\n\n\n<p>Vesel\u012bbas apr\u016bp\u0113 noskaidro\u0161anas novirze var rad\u012bt nopietnas sekas. Ja medic\u012bnas p\u0113t\u012bjumos uzman\u012bba tiek piev\u0113rsta tikai smagiem slim\u012bbas gad\u012bjumiem, \u0101rsti var p\u0101rv\u0113rt\u0113t slim\u012bbas b\u012bstam\u012bbu. Tas var novest pie p\u0101rm\u0113r\u012bgas \u0101rst\u0113\u0161anas vai nevajadz\u012bgas iejauk\u0161an\u0101s pacientiem ar viegliem simptomiem. No otras puses, ja tiek nepietiekami zi\u0146ots par viegliem gad\u012bjumiem, vesel\u012bbas apr\u016bpes sniedz\u0113ji var neuztvert slim\u012bbu pietiekami nopietni, kas var novest pie nepietiekamas \u0101rst\u0113\u0161anas.<\/p>\n\n\n\n<h4>Sabiedrisk\u0101 politika<\/h4>\n\n\n\n<p>Politikas veidot\u0101ji, pie\u0146emot l\u0113mumus par sabiedr\u012bbas vesel\u012bbu, izgl\u012bt\u012bbu un cit\u0101m svar\u012bg\u0101m jom\u0101m, bie\u017ei pa\u013caujas uz datiem. Ja vi\u0146u izmantotie dati ir neobjekt\u012bvi, vi\u0146u izstr\u0101d\u0101t\u0101 politika var b\u016bt neefekt\u012bva vai pat kait\u012bga.&nbsp;<\/p>\n\n\n\n<h4>Uz\u0146\u0113m\u0113jdarb\u012bba<\/h4>\n\n\n\n<p>Uz\u0146\u0113m\u0113jdarb\u012bbas pasaul\u0113 noskaidro\u0161anas aizspriedumi var novest pie k\u013c\u016bdainas tirgus izp\u0113tes un sliktu l\u0113mumu pie\u0146em\u0161anas. Ja uz\u0146\u0113mums aptauj\u0101 tikai savus loj\u0101l\u0101kos klientus, tas var secin\u0101t, ka t\u0101 produkti ir visp\u0101r\u0113ji iecien\u012bti, lai gan paties\u012bb\u0101 daudziem potenci\u0101lajiem klientiem var b\u016bt negat\u012bvs viedoklis. Tas var novest pie nepareiz\u0101m m\u0101rketinga strat\u0113\u0123ij\u0101m vai produktu izstr\u0101des l\u0113mumiem, kas neatbilst pla\u0161\u0101ka tirgus vajadz\u012bb\u0101m.<\/p>\n\n\n\n<h4>Izgl\u012bt\u012bba<\/h4>\n\n\n\n<p>Izgl\u012bt\u012bb\u0101 noskaidro\u0161anas novirze var ietekm\u0113t p\u0113t\u012bjumus par skol\u0113nu sekm\u0113m, m\u0101c\u012bbu metod\u0113m vai m\u0101c\u012bbu l\u012bdzek\u013ciem. Ja p\u0113t\u012bjumi koncentr\u0113jas tikai uz skol\u0113niem ar labiem sasniegumiem, tie var ne\u0146emt v\u0113r\u0101 probl\u0113mas, ar kur\u0101m saskaras skol\u0113ni, kuriem kl\u0101jas gr\u016bti, un rezult\u0101t\u0101 var tikt izdar\u012bti secin\u0101jumi, kas neattiecas uz visu skol\u0113nu kopumu. T\u0101 rezult\u0101t\u0101 var tikt izstr\u0101d\u0101tas t\u0101das izgl\u012bt\u012bbas programmas vai politika, kas neatbalsta visus skol\u0113nus.<\/p>\n\n\n\n<p>Lai nodro\u0161in\u0101tu, ka j\u016bsu p\u0113t\u012bjums un secin\u0101jumi ir prec\u012bzi un atspogu\u013co visu ainu, ir svar\u012bgi identific\u0113t ieg\u016b\u0161anas novirzi. Mekl\u0113jot t\u0101das paz\u012bmes k\u0101 selekt\u012bvi datu avoti, tr\u016bksto\u0161a inform\u0101cija un atsevi\u0161\u0137u grupu p\u0101rm\u0113r\u012bga p\u0101rst\u0101v\u012bba, j\u016bs varat atpaz\u012bt, kad neobjektivit\u0101te ietekm\u0113 j\u016bsu datus.&nbsp;<\/p>\n\n\n\n<p><strong>Lasiet ar\u012b: <\/strong><a href=\"https:\/\/mindthegraph.com\/blog\/observer-bias\/\"><strong>Nov\u0113rot\u0101ja neobjektivit\u0101tes p\u0101rvar\u0113\u0161ana p\u0113tniec\u012bb\u0101: K\u0101 to mazin\u0101t?<\/strong><\/a><\/p>\n\n\n\n<h2>Strat\u0113\u0123ijas noskaidro\u0161anas neobjektivit\u0101tes mazin\u0101\u0161anai<\/h2>\n\n\n\n<p>Ja v\u0113laties nodro\u0161in\u0101t, ka dati, ar kuriem str\u0101d\u0101jat, prec\u012bzi atspogu\u013co realit\u0101ti, kuru m\u0113\u0123in\u0101t izprast, ir svar\u012bgi nov\u0113rst noskaidro\u0161anas novirzi. Noskaidro\u0161anas neobjektivit\u0101te var iezagties j\u016bsu p\u0113t\u012bjum\u0101, ja da\u017ei datu veidi ir p\u0101rst\u0101v\u0113ti p\u0101r\u0101k liel\u0101 vai maz\u0101k\u0101 m\u0113r\u0101, t\u0101d\u0113j\u0101di radot izkrop\u013cotus rezult\u0101tus.&nbsp;<\/p>\n\n\n\n<p>Tom\u0113r ir vair\u0101kas strat\u0113\u0123ijas un metodes, ko varat izmantot, lai mazin\u0101tu \u0161o neobjektivit\u0101ti un uzlabotu datu v\u0101k\u0161anas un anal\u012bzes ticam\u012bbu.<\/p>\n\n\n\n<h3>Neobjektivit\u0101tes mazin\u0101\u0161anas strat\u0113\u0123ijas<\/h3>\n\n\n\n<p>Ja p\u0113t\u012bjum\u0101 vai datu v\u0101k\u0161an\u0101 v\u0113laties mazin\u0101t noskaidro\u0161anas neobjektivit\u0101ti, ir vair\u0101ki praktiski so\u013ci un strat\u0113\u0123ijas, ko varat \u012bstenot. \u0145emot v\u0113r\u0101 iesp\u0113jamos neobjektivit\u0101tes faktorus un izmantojot \u0161os pa\u0146\u0113mienus, j\u016bs varat padar\u012bt savus datus prec\u012bz\u0101kus un reprezentat\u012bv\u0101kus.<\/p>\n\n\n\n<h4>Izlases veido\u0161ana p\u0113c nejau\u0161\u012bbas principa<\/h4>\n\n\n\n<p>Viens no visefekt\u012bv\u0101kajiem veidiem, k\u0101 samazin\u0101t ieg\u016b\u0161anas novirzi, ir izmantot <a href=\"https:\/\/mindthegraph.com\/blog\/simple-random-sampling\/\">izlases veida paraugu \u0146em\u0161ana<\/a>. Tas nodro\u0161ina, ka ikvienam iedz\u012bvot\u0101ju grupas p\u0101rst\u0101vim ir vien\u0101das iesp\u0113jas tikt iek\u013cautam p\u0113t\u012bjum\u0101, t\u0101d\u0113j\u0101di nov\u0113r\u0161ot k\u0101das grupas p\u0101r\u0101k lielu p\u0101rst\u0101v\u012bbu.&nbsp;<\/p>\n\n\n\n<p>Piem\u0113ram, ja veicat aptauju par \u0113\u0161anas paradumiem, nejau\u0161\u0101s izlases metode ietver nejau\u0161u dal\u012bbnieku atlasi, nekoncentr\u0113joties uz k\u0101du konkr\u0113tu grupu, piem\u0113ram, sporta z\u0101les apmekl\u0113t\u0101jiem vai cilv\u0113kiem, kuri jau iev\u0113ro vesel\u012bgu uzturu. \u0160\u0101d\u0101 veid\u0101 j\u016bs varat ieg\u016bt prec\u012bz\u0101ku visas popul\u0101cijas p\u0101rst\u0101v\u012bbu.<\/p>\n\n\n\n<p><strong>Lasiet ar\u012b: <\/strong><a href=\"https:\/\/mindthegraph.com\/blog\/sampling-bias\/\"><strong>Probl\u0113ma, ko sauc par izlases novirzi<\/strong><\/a><\/p>\n\n\n\n<h4>Paraugu daudzveid\u012bbas palielin\u0101\u0161ana<\/h4>\n\n\n\n<p>V\u0113l viens svar\u012bgs solis ir nodro\u0161in\u0101t, lai j\u016bsu paraugs b\u016btu daudzveid\u012bgs. Tas noz\u012bm\u0113 akt\u012bvi mekl\u0113t dal\u012bbniekus vai datu avotus ar visda\u017e\u0101d\u0101ko izcelsmi, pieredzi un apst\u0101k\u013ciem. Piem\u0113ram, ja p\u0113t\u0101t jaunu medikamentu ietekmi, p\u0101rliecinieties, ka taj\u0101 ir iek\u013cauti da\u017e\u0101da vecuma, dzimuma un vesel\u012bbas st\u0101vok\u013ca cilv\u0113ki, lai izvair\u012btos no koncentr\u0113\u0161an\u0101s tikai uz vienu grupu. Jo daudzveid\u012bg\u0101ka b\u016bs j\u016bsu izlase, jo ticam\u0101ki b\u016bs j\u016bsu secin\u0101jumi.<\/p>\n\n\n\n<h4>Veikt garengriezuma p\u0113t\u012bjumus<\/h4>\n\n\n\n<p>Garengriezuma p\u0113t\u012bjums ir t\u0101ds, kur\u0101 dal\u012bbnieki tiek nov\u0113roti noteiktu laika periodu, v\u0101cot datus vair\u0101kos punktos. \u0160\u0101da pieeja var pal\u012bdz\u0113t noteikt jebk\u0101das izmai\u0146as vai tendences, kas var\u0113tu tikt nepaman\u012btas, v\u0101cot datus tikai vienu reizi. Izsekojot datus laika gait\u0101, j\u016bs varat ieg\u016bt piln\u012bg\u0101ku priek\u0161statu un samazin\u0101t neobjektivit\u0101tes iesp\u0113jam\u012bbu, jo tas \u013cauj jums redz\u0113t, k\u0101 main\u0101s faktori, nevis izdar\u012bt pie\u0146\u0113mumus, pamatojoties uz vienu momentuz\u0146\u0113mumu.<\/p>\n\n\n\n<h4>Aklie vai dubultaklie p\u0113t\u012bjumi<\/h4>\n\n\n\n<p>Da\u017eos gad\u012bjumos, jo \u012bpa\u0161i medic\u012bniskos vai psiholo\u0123iskos p\u0113t\u012bjumos, akls p\u0113t\u012bjums ir efekt\u012bvs veids, k\u0101 samazin\u0101t neobjektivit\u0101ti. Vienreiz\u0113ji akls p\u0113t\u012bjums noz\u012bm\u0113, ka dal\u012bbnieki nezina, kur\u0101 grup\u0101 vi\u0146i ir (piem\u0113ram, vai vi\u0146i sa\u0146em \u0101rst\u0113\u0161anu vai placebo).&nbsp;<\/p>\n\n\n\n<p>Dubultakl\u0101 p\u0113t\u012bjum\u0101 tiek sperts v\u0113l viens solis t\u0101l\u0101k, nodro\u0161inot, ka gan dal\u012bbnieki, gan p\u0113tnieki nezina, kur\u0161 ir kur\u0101 grup\u0101. Tas var pal\u012bdz\u0113t nov\u0113rst gan apzin\u0101tu, gan neapzin\u0101tu neobjektivit\u0101ti, kas ietekm\u0113 rezult\u0101tus.<\/p>\n\n\n\n<h4>Izmantojiet kontroles grupas<\/h4>\n\n\n\n<p>Iek\u013caujot sav\u0101 p\u0113t\u012bjum\u0101 kontroles grupu, varat sal\u012bdzin\u0101t \u0101rst\u0113t\u0101s grupas rezult\u0101tus ar tiem, kas nav pak\u013cauti intervencei. \u0160is sal\u012bdzin\u0101jums var pal\u012bdz\u0113t jums noteikt, vai rezult\u0101ti ir saist\u012bti ar pa\u0161u intervenci, vai ar\u012b tos ietekm\u0113 citi faktori. Kontrolgrupas nodro\u0161ina atskaites punktu, kas pal\u012bdz mazin\u0101t neobjektivit\u0101ti, sniedzot skaidr\u0101ku priek\u0161statu par to, kas notiktu bez intervences.<\/p>\n\n\n\n<h4>Izm\u0113\u0123in\u0101juma p\u0113t\u012bjumi<\/h4>\n\n\n\n<p>Izm\u0113\u0123in\u0101juma p\u0113t\u012bjuma veik\u0161ana pirms pilna m\u0113roga p\u0113t\u012bjuma uzs\u0101k\u0161anas var pal\u012bdz\u0113t jums jau pa\u0161\u0101 s\u0101kum\u0101 identific\u0113t iesp\u0113jamos noskaidro\u0161anas novirzes avotus.&nbsp;<\/p>\n\n\n\n<p>Izm\u0113\u0123in\u0101juma p\u0113t\u012bjums ir maz\u0101ka, izm\u0113\u0123in\u0101juma versija, kas \u013cauj jums p\u0101rbaud\u012bt savas metodes un noskaidrot, vai datu v\u0101k\u0161anas proces\u0101 nav nepiln\u012bbu. Tas dod jums iesp\u0113ju veikt korekcijas, pirms veikt liel\u0101ku p\u0113t\u012bjumu, t\u0101d\u0113j\u0101di samazinot gal\u012bgo rezult\u0101tu neobjektivit\u0101tes risku.<\/p>\n\n\n\n<h4>P\u0101rredzama zi\u0146o\u0161ana<\/h4>\n\n\n\n<p>Lai mazin\u0101tu neobjektivit\u0101ti, \u013coti svar\u012bga ir p\u0101rredzam\u012bba. Atkl\u0101ti past\u0101stiet par datu v\u0101k\u0161anas metod\u0113m, paraugu \u0146em\u0161anas pa\u0146\u0113mieniem un jebk\u0101diem iesp\u0113jamiem p\u0113t\u012bjuma ierobe\u017eojumiem. Skaidri nor\u0101dot darb\u012bbas jomu un ierobe\u017eojumus, j\u016bs \u013caujat citiem kritiski izv\u0113rt\u0113t j\u016bsu darbu un saprast, kur var\u0113tu b\u016bt neobjektivit\u0101te. \u0160\u0101da atkl\u0101t\u012bba pal\u012bdz vairot uztic\u0113\u0161anos un \u013cauj citiem atk\u0101rtot vai papildin\u0101t j\u016bsu p\u0113t\u012bjumu ar prec\u012bz\u0101kiem datiem.<\/p>\n\n\n\n<h3>Tehnolo\u0123iju loma<\/h3>\n\n\n\n<p>Tehnolo\u0123ijai var b\u016bt noz\u012bm\u012bga loma, pal\u012bdzot jums identific\u0113t un samazin\u0101t noskaidro\u0161anas novirzi. Izmantojot progres\u012bvus r\u012bkus un metodes, varat efekt\u012bv\u0101k analiz\u0113t datus, paman\u012bt iesp\u0113jamos novirzes un labot t\u0101s, pirms t\u0101s ietekm\u0113 secin\u0101jumus.<\/p>\n\n\n\n<h4>Datu anal\u012bzes programmat\u016bra<\/h4>\n\n\n\n<p>Viens no sp\u0113c\u012bg\u0101kajiem instrumentiem neobjektivit\u0101tes mazin\u0101\u0161anai ir datu anal\u012bzes programmat\u016bra. \u0160\u012bs programmas var \u0101tri apstr\u0101d\u0101t lielus datu apjomus, pal\u012bdzot jums noteikt mode\u013cus vai neatbilst\u012bbas, kas var\u0113tu liecin\u0101t par neobjektivit\u0101ti.&nbsp;<\/p>\n\n\n\n<h4>Ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s algoritmi<\/h4>\n\n\n\n<p>Ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s algoritmi var b\u016bt \u013coti noder\u012bgi, lai atkl\u0101tu un kori\u0123\u0113tu neobjektivit\u0101ti datos. \u0160os algoritmus var apm\u0101c\u012bt, lai atpaz\u012btu, kad noteiktas grupas ir nepietiekami p\u0101rst\u0101v\u0113tas vai kad datu punkti ir izkrop\u013coti noteikt\u0101 virzien\u0101. Kad algoritms identific\u0113 neobjektivit\u0101ti, tas var attiec\u012bgi piel\u0101got datu v\u0101k\u0161anas vai anal\u012bzes procesu, nodro\u0161inot, ka gal\u012bgie rezult\u0101ti ir prec\u012bz\u0101ki.<\/p>\n\n\n\n<h4>Automatiz\u0113ti datu v\u0101k\u0161anas r\u012bki<\/h4>\n\n\n\n<p>Automatiz\u0113ti datu v\u0101k\u0161anas r\u012bki var pal\u012bdz\u0113t samazin\u0101t cilv\u0113ku k\u013c\u016bdas un neobjektivit\u0101ti datu v\u0101k\u0161anas proces\u0101. Piem\u0113ram, ja veicat tie\u0161saistes aptauju, varat izmantot programmat\u016bru, kas nejau\u0161i atlasa dal\u012bbniekus vai autom\u0101tiski nodro\u0161ina, ka izlas\u0113 tiek iek\u013cautas da\u017e\u0101das grupas.<\/p>\n\n\n\n<h4>Statistisk\u0101s korekcijas metodes<\/h4>\n\n\n\n<p>Da\u017eos gad\u012bjumos var izmantot statistisk\u0101s korekcijas metodes, lai kori\u0123\u0113tu novirzes p\u0113c tam, kad dati jau ir sav\u0101kti. Piem\u0113ram, p\u0113tnieki var izmantot t\u0101dus pa\u0146\u0113mienus k\u0101 sv\u0113r\u0161ana vai imput\u0101cija, lai kori\u0123\u0113tu nepietiekami p\u0101rst\u0101v\u0113to grupu datus. Sv\u0113r\u0161ana ietver to, ka nepietiekami p\u0101rst\u0101v\u0113to grupu datiem pie\u0161\u0137ir liel\u0101ku noz\u012bmi, lai l\u012bdzsvarotu izlasi.&nbsp;<\/p>\n\n\n\n<h4>Re\u0101llaika uzraudz\u012bbas r\u012bki<\/h4>\n\n\n\n<p>Re\u0101l\u0101 laika uzraudz\u012bbas r\u012bki \u013cauj jums sekot l\u012bdzi datu v\u0101k\u0161anai, kad t\u0101 notiek, un \u013cauj jums paman\u012bt neobjektivit\u0101ti, tikl\u012bdz t\u0101 par\u0101d\u0101s. Piem\u0113ram, ja veicat liela m\u0113roga p\u0113t\u012bjumu, kur\u0101 dati tiek v\u0101kti vair\u0101kus m\u0113ne\u0161us, re\u0101l\u0101 laika uzraudz\u012bba var br\u012bdin\u0101t, ja da\u017eas grupas ir nepietiekami p\u0101rst\u0101v\u0113tas vai ja dati s\u0101k novirz\u012bties vien\u0101 virzien\u0101.<\/p>\n\n\n\n<p>Lai nodro\u0161in\u0101tu j\u016bsu p\u0113t\u012bjuma uzticam\u012bbu un precizit\u0101ti, ir \u013coti svar\u012bgi nov\u0113rst noskaidro\u0161anas novirzi. Iev\u0113rojot t\u0101das praktiskas strat\u0113\u0123ijas k\u0101 izlases veida paraugu \u0146em\u0161ana, izlases daudzveid\u012bbas palielin\u0101\u0161ana un kontroles grupu izmanto\u0161ana, j\u016bs varat samazin\u0101t neobjektivit\u0101tes iesp\u0113jam\u012bbu datu v\u0101k\u0161an\u0101.&nbsp;<\/p>\n\n\n\n<p>Nobeigum\u0101 j\u0101secina, ka, lai nodro\u0161in\u0101tu, ka sav\u0101ktie un analiz\u0113tie dati ir prec\u012bzi un uzticami, ir svar\u012bgi nov\u0113rst noskaidro\u0161anas neobjektivit\u0101ti. \u012astenojot t\u0101das strat\u0113\u0123ijas k\u0101 izlases veida atlase, palielinot izlases daudzveid\u012bbu, veicot garengriezuma un izm\u0113\u0123in\u0101juma p\u0113t\u012bjumus un izmantojot kontroles grupas, j\u016bs varat iev\u0113rojami samazin\u0101t neobjektivit\u0101tes iesp\u0113jam\u012bbu sav\u0101 p\u0113t\u012bjum\u0101.&nbsp;<\/p>\n\n\n\n<p>Kop\u0101 \u0161\u012bs metodes pal\u012bdz ieg\u016bt prec\u012bz\u0101kus un reprezentat\u012bv\u0101kus rezult\u0101tus, t\u0101d\u0113j\u0101di uzlabojot j\u016bsu p\u0113t\u012bjumu rezult\u0101tu kvalit\u0101ti un der\u012bgumu.<\/p>\n\n\n\n<p><strong>Saist\u012bts raksts:<\/strong>&nbsp; <a href=\"https:\/\/mindthegraph.com\/blog\/how-to-avoid-bias-in-research\/\"><strong>K\u0101 izvair\u012bties no neobjektivit\u0101tes p\u0113tniec\u012bb\u0101: K\u0101 r\u012bkoties, lai izvair\u012btos no neobjektivit\u0101tes?<\/strong><\/a><\/p>\n\n\n\n<h2>Zin\u0101tniskie skait\u013ci, grafiskie kopsavilkumi un infografikas j\u016bsu p\u0113t\u012bjumiem<\/h2>\n\n\n\n<p>Vai j\u016bs mekl\u0113jat zin\u0101tnes skait\u013cus, grafiskus kopsavilkumus un infografikas vienuviet? L\u016bk, \u0161eit tas ir! <a href=\"https:\/\/mindthegraph.com\/science-figures\/?utm_source=blog&amp;utm_medium=cta-final&amp;utm_campaign=conversion\">Mind the Graph<\/a> pied\u0101v\u0101 vizu\u0101lo materi\u0101lu kolekciju, kas lieliski noder\u0113s j\u016bsu p\u0113t\u012bjumiem. J\u016bs varat izv\u0113l\u0113ties k\u0101du no platform\u0101 eso\u0161aj\u0101m iepriek\u0161 sagatavotaj\u0101m grafik\u0101m un piel\u0101got to atbilsto\u0161i sav\u0101m vajadz\u012bb\u0101m. J\u016bs pat varat sa\u0146emt pal\u012bdz\u012bbu no m\u016bsu dizaineriem un izveidot \u012bpa\u0161us kopsavilkumus, pamatojoties uz j\u016bsu p\u0113t\u012bjuma t\u0113mu. T\u0101tad, kas ir gaid\u012b\u0161ana? Re\u0123istr\u0113jieties Mind the Graph jau tagad un uzveiciet savu p\u0113t\u012bjumu.<\/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 - Zin\u0101tnes infografikas veidot\u0101js\" 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\">Izp\u0113tiet zin\u0101\u0161anu un atzi\u0146u dzi\u013cumus, izmantojot \u0161o aizraujo\u0161o videoklipu. \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>Re\u0123istr\u0113jieties Mind the Graph<\/strong><\/a><\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Uzziniet vair\u0101k par ieg\u016b\u0161anas novirzi, t\u0101s c\u0113lo\u0146iem un praktisk\u0101m strat\u0113\u0123ij\u0101m, k\u0101 nov\u0113rst datu izkrop\u013cojumus p\u0113tniec\u012bb\u0101.<\/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\/lv\/ascertainment-bias\/\" \/>\n<meta property=\"og:locale\" content=\"lv_LV\" \/>\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\/lv\/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\/lv\/ascertainment-bias\/","og_locale":"lv_LV","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\/lv\/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":"lv","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":"lv"},{"@type":"Person","@id":"https:\/\/mindthegraph.com\/blog\/#\/schema\/person\/1809367ac22d998ef1780e61c942bd9e","name":"Sowjanya Pedada","image":{"@type":"ImageObject","inLanguage":"lv","@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\/lv\/author\/sowjanya\/"}]}},"_links":{"self":[{"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/posts\/55859"}],"collection":[{"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/users\/33"}],"replies":[{"embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/comments?post=55859"}],"version-history":[{"count":1,"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/posts\/55859\/revisions"}],"predecessor-version":[{"id":55863,"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/posts\/55859\/revisions\/55863"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/media\/55860"}],"wp:attachment":[{"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/media?parent=55859"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/categories?post=55859"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/lv\/wp-json\/wp\/v2\/tags?post=55859"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}