{"id":26638,"date":"2023-02-08T09:50:00","date_gmt":"2023-02-08T12:50:00","guid":{"rendered":"https:\/\/mindthegraph.com\/blog\/the-science-behind-falling-in-love-copy\/"},"modified":"2023-04-05T10:55:10","modified_gmt":"2023-04-05T13:55:10","slug":"statistics-parametric-tests","status":"publish","type":"post","link":"https:\/\/mindthegraph.com\/blog\/lt\/statistics-parametric-tests\/","title":{"rendered":"Kas yra statistiniai parametriniai testai ir kur juos taikyti?"},"content":{"rendered":"

Statistiniai parametriniai testai - tai statistin\u0117s analiz\u0117s r\u016b\u0161is, naudojama hipotez\u0117ms apie populiacijos vidurk\u012f ir dispersij\u0105 tikrinti. \u0160ie testai grind\u017eiami prielaida, kad pagrindiniai duomenys pasiskirsto pagal normal\u0173j\u012f skirstin\u012f, ir pasi\u017eymi keliomis pagrindin\u0117mis savyb\u0117mis, \u012fskaitant tvirtum\u0105, patikimum\u0105 ir geb\u0117jim\u0105 aptikti ne\u017eymius duomen\u0173 skirtumus.<\/p>\n\n\n\n

Parametriniai testai da\u017enai naudojami \u012fvairiose srityse, \u012fskaitant medicinos, rinkos ir socialinius mokslus. \u0160iose srityse tyr\u0117jai gali naudoti parametrinius testus, nor\u0117dami nustatyti populiacijos vidurki\u0173 ar dispersij\u0173 poky\u010di\u0173 reik\u0161mingum\u0105 arba nustatyti, ar tam tikras gydymas ar intervencija tur\u0117jo reik\u0161ming\u0105 poveik\u012f duomenims.<\/p>\n\n\n\n

Da\u017eniausiai pasitaikantys statistikos tipai parametriniai testai<\/h2>\n\n\n\n

T testas <\/h3>\n\n\n\n

Vienas i\u0161 da\u017eniausiai naudojam\u0173 parametrini\u0173 test\u0173 yra t-testas, kuris naudojamas dviej\u0173 populiacij\u0173 vidurkiams palyginti. . t-testas<\/a> daroma prielaida, kad pagrindiniai duomenys pasiskirst\u0119 normaliai ir kad abiej\u0173 populiacij\u0173 dispersijos yra lygios. Testo statistika apskai\u010diuojama naudojant dviej\u0173 populiacij\u0173 vidurki\u0173 skirtum\u0105, padalyt\u0105 i\u0161 skirtumo standartin\u0117s paklaidos.<\/p>\n\n\n\n

ANOVA testas<\/h3>\n\n\n\n

Kitas da\u017enas parametrinis testas yra dispersin\u0117 analiz\u0117 (ANOVA), kuri naudojama trij\u0173 ar daugiau populiacij\u0173 vidurkiams palyginti. . ANOVA testas<\/a> daroma prielaida, kad pagrindiniai duomenys pasiskirst\u0119 normaliai ir kad vis\u0173 populiacij\u0173 dispersijos yra lygios. Testo statistika apskai\u010diuojama pagal populiacij\u0173 dispersij\u0173 ir populiacij\u0173 viduje esan\u010di\u0173 dispersij\u0173 santyk\u012f.<\/p>\n\n\n\n

Kiti parametriniai testai<\/h3>\n\n\n\n

Be t-testo ir ANOVA, yra keletas kit\u0173 statistini\u0173 parametrini\u0173 test\u0173, kurie naudojami \u012fvairiose srityse, \u012fskaitant porin\u012f t-test\u0105, vienpus\u012f ANOVA, dvipus\u012f ANOVA, pakartotini\u0173 matavim\u0173 ANOVA ir mi\u0161raus plano ANOVA. Kiekvienas i\u0161 \u0161i\u0173 test\u0173 turi skirtingas prielaidas ir test\u0173 statistik\u0105 ir yra naudojamas sprend\u017eiant skirtingo pob\u016bd\u017eio tyrimo klausimus.<\/p>\n\n\n\n

Vienas i\u0161 pagrindini\u0173 parametrini\u0173 test\u0173 privalum\u0173 yra tai, kad jie yra patikimi, t. y. jie n\u0117ra jautr\u016bs pagrindini\u0173 duomen\u0173 pasiskirstymo formai. Jei duomenys pasiskirst\u0119 apytikriai normaliai, parametriniai testai gali duoti tikslius rezultatus.<\/p>\n\n\n\n

Per kelias minutes sukurkite nuostabi\u0173 infografik\u0173<\/h2>\n\n\n\n

Mind the Graph yra puikus \u012frankis duomenims sujungti ir vizualiai pateikti. Naudokite diagramas, lenteles ir mokslines iliustracijas, kad savo darb\u0105 lengviau suprastum\u0117te.<\/p>\n\n\n\n

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Sukurti pirm\u0105j\u012f infografik\u0105<\/a><\/div>\n<\/div>\n\n\n\n
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Statistini\u0173 parametrini\u0173 test\u0173 patikimumas<\/h2>\n\n\n\n

Kitas parametrini\u0173 test\u0173 privalumas - j\u0173 patikimumas, nes jie pagr\u012fsti nusistov\u0117jusiais statistiniais metodais ir prielaidomis. Parametrini\u0173 test\u0173 rezultatai yra labai lengvai pakartojami ir gali b\u016bti naudojami pagr\u012fstoms i\u0161vadoms apie pagrindin\u0119 populiacij\u0105 daryti.<\/p>\n\n\n\n

Nepaisant daugyb\u0117s privalum\u0173, parametriniai testai ne visada yra geriausias pasirinkimas kiekvienam duomen\u0173 rinkiniui. Kai kuriais atvejais baziniai duomenys gali b\u016bti pasiskirst\u0119 netaisyklingai arba populiacij\u0173 dispersijos gali b\u016bti nevienodos. Tokiais atvejais labiau tinka neparametriniai testai.<\/p>\n\n\n\n

Parametriniai testai ir neparametriniai testai<\/h2>\n\n\n\n

Neparametriniai testai - tai statistin\u0117s analiz\u0117s r\u016b\u0161is, kai nedaroma joki\u0173 prielaid\u0173 apie pagrindin\u012f duomen\u0173 pasiskirstym\u0105. Vietoj to, nustatant rezultat\u0173 reik\u0161mingum\u0105, jie remiasi duomen\u0173 rangu. Kai kurie \u012fprasti neparametriniai testai yra \u0161ie Vilkoksono rang\u0173 sumos testas<\/a>. Kruskal-Wallis testas<\/a>, ir Manno-Vitnio testas<\/a>.<\/p>\n\n\n\n

Renkantis tarp parametrini\u0173 ir neparametrini\u0173 test\u0173, svarbu atsi\u017evelgti \u012f duomen\u0173 pob\u016bd\u012f ir sprend\u017eiam\u0105 tyrimo klausim\u0105. Apskritai parametriniai testai tinka duomenims, kurie yra normaliai pasiskirst\u0119 ir turi vienodas dispersijas, o neparametriniai testai tinka duomenims, kurie neatitinka \u0161i\u0173 prielaid\u0173.<\/p>\n\n\n\n

Statistinio parametrinio testo pavyzdys<\/h2>\n\n\n\n

Tarkime, kad tyr\u0117jas nori patikrinti, ar skiriasi dviej\u0173 vaik\u0173 grupi\u0173 - A ir B grupi\u0173 - vidutinis \u016bgis. Tam tyr\u0117jas atsitiktinai i\u0161 kiekvienos grup\u0117s atrenka po 20 vaik\u0173 ir i\u0161matuoja j\u0173 \u016bg\u012f.<\/p>\n\n\n\n

Tyr\u0117jas nori su\u017einoti, ar A grup\u0117s vaik\u0173 \u016bgio vidurkis skiriasi nuo B grup\u0117s vaik\u0173 \u016bgio vidurkio vidurkio. Atliekant t-test\u0105 daroma prielaida, kad pagrindiniai duomenys pasiskirst\u0119 normaliai ir kad abiej\u0173 grupi\u0173 dispersijos yra lygios.<\/p>\n\n\n\n

Tyr\u0117jas apskai\u010diuoja kiekvienos grup\u0117s \u016bgio vidurk\u012f ir nustato, kad A grup\u0117s \u016bgio vidurkis yra 150 cm, o B grup\u0117s - 155 cm. Tada tyr\u0117jas apskai\u010diuoja kiekvienos grup\u0117s standartin\u012f nuokryp\u012f ir nustato, kad A grup\u0117s standartinis nuokrypis yra 5 cm, o B grup\u0117s - 4 cm.<\/p>\n\n\n\n

Tada tyr\u0117jas apskai\u010diuoja t-statistik\u0105, naudodamas dviej\u0173 grupi\u0173 vidurki\u0173 skirtum\u0105, padalyt\u0105 i\u0161 skirtumo standartin\u0117s paklaidos. Jei t-statistika yra didesn\u0117 u\u017e kritin\u0119 reik\u0161m\u0119, nustatyt\u0105 pagal reik\u0161mingumo lygmen\u012f ir laisv\u0117s laipsnius, tyr\u0117jas gali daryti i\u0161vad\u0105, kad A ir B grupi\u0173 vaik\u0173 \u016bgio vidurkiai reik\u0161mingai skiriasi.<\/p>\n\n\n\n

\u0160iame pavyzdyje parodyta, kaip dviej\u0173 im\u010di\u0173 t-testu galima patikrinti hipotez\u0119 apie dviej\u0173 grupi\u0173 vidurki\u0173 skirtum\u0105. T-testas yra galingas ir pla\u010diai naudojamas parametrinis testas, kuriuo galima patikimai ir patikimai patikrinti hipotezes apie populiacijos vidurk\u012f.<\/p>\n\n\n\n

Galingi duomen\u0173 analiz\u0117s \u012frankiai<\/h2>\n\n\n\n

Apibendrinant galima daryti i\u0161vad\u0105, kad parametriniai testai yra galinga statistin\u0117s analiz\u0117s priemon\u0117, leid\u017eianti gauti tvirtus ir patikimus rezultatus \u012fvairiose srityse. Ta\u010diau svarbu pasirinkti tinkam\u0105 test\u0105, atsi\u017evelgiant \u012f duomen\u0173 pob\u016bd\u012f ir sprend\u017eiam\u0105 tyrimo klausim\u0105. Nesvarbu, ar naudojami parametriniai, ar neparametriniai testai, statistin\u0117s analiz\u0117s tikslas visada yra padaryti pagr\u012fstas i\u0161vadas apie tiriam\u0105j\u0105 populiacij\u0105 ir i\u0161 duomen\u0173 padaryti prasmingas i\u0161vadas.<\/p>\n\n\n\n

Niekas negali pranokti nepriekai\u0161tingo vaizdinio k\u016brinio, kuriuo perteikiama sud\u0117tinga \u017einut\u0117.<\/h2>\n\n\n\n

Sunkiai perduodate didel\u012f kiek\u012f informacijos? Naudokite infografik\u0105 ir iliustracijas, kad darbas b\u016bt\u0173 suprantamesnis ir prieinamesnis. Mind the Graph yra puiki priemon\u0117 tyr\u0117jams, norintiems savo darb\u0105 padaryti efektyvesn\u012f naudojant vizualiai patrauklias infografikas. <\/p>\n\n\n\n

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Iliustracijomis pagausinkite mano tyrimus<\/a><\/div>\n<\/div>\n\n\n\n
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<\/p>","protected":false},"excerpt":{"rendered":"

\u0160is straipsnis pad\u0117s jums suprasti statistinius parametrinius testus, da\u017eniausiai pasitaikan\u010dius j\u0173 tipus, taip pat kur ir kada juos taikyti.<\/p>","protected":false},"author":4,"featured_media":26646,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[959,28],"tags":[],"yoast_head":"\nWhat are statistics parametric tests and where to apply them?<\/title>\n<meta name=\"description\" content=\"This article will help you understand statistics parametric tests, their most common types, and also where and when to apply them.\" \/>\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\/lt\/statistics-parametric-tests\/\" \/>\n<meta property=\"og:locale\" content=\"lt_LT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What 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He has a Ph.D. and solid scientific background in Psychopharmacology and experience as a Guest Researcher at the Max Planck Institute of Psychiatry (Germany) and Researcher in D'Or Institute for Research and Education (IDOR, Brazil). Fabricio holds over 2500 citations in Google Scholar. He has 10 years of experience in small innovative businesses, with relevant experience in product design and innovation management. 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