{"id":50301,"date":"2024-02-11T11:03:02","date_gmt":"2024-02-11T14:03:02","guid":{"rendered":"https:\/\/mindthegraph.com\/blog\/machine-learning-in-science-copy\/"},"modified":"2024-02-07T11:16:52","modified_gmt":"2024-02-07T14:16:52","slug":"post-hoc-testing-anova","status":"publish","type":"post","link":"https:\/\/mindthegraph.com\/blog\/tr\/anova-sonrasi-test\/","title":{"rendered":"Post Hoc Test ANOVA: Veri Setlerinin Nas\u0131l Analiz Edilece\u011fini \u00d6\u011frenin"},"content":{"rendered":"<p>Hi\u00e7 ara\u015ft\u0131rmac\u0131lar\u0131n ilk bak\u0131\u015fta eski bir kod kadar gizemli g\u00f6r\u00fcnen veri gruplar\u0131ndan nas\u0131l somut sonu\u00e7lar \u00e7\u0131kard\u0131\u011f\u0131n\u0131 merak ettiniz mi? ANOVA - Varyans Analizi ba\u011flam\u0131nda post hoc testinin arkas\u0131ndaki sihri anlad\u0131\u011f\u0131n\u0131zda bu gizem biraz daha azal\u0131r. Bu istatistiksel y\u00f6ntem sadece bir ara\u00e7 de\u011fildir; Sherlock Holmes'un say\u0131s\u0131z say\u0131 i\u00e7indeki gizli ger\u00e7ekleri ortaya \u00e7\u0131karmak i\u00e7in kulland\u0131\u011f\u0131 b\u00fcy\u00fctecine benzer. \u0130ster tez verilerinizle bo\u011fu\u015fan bir \u00f6\u011frenci, ister sa\u011flam sonu\u00e7lar elde etmeyi hedefleyen deneyimli bir ara\u015ft\u0131rmac\u0131 olun, post hoc testlerin g\u00fcc\u00fcn\u00fc ortaya \u00e7\u0131karmak bulgular\u0131n\u0131z\u0131 ilgin\u00e7 olmaktan \u00e7\u0131kar\u0131p \u00e7\u0131\u011f\u0131r a\u00e7\u0131c\u0131 hale getirebilir.<\/p>\n\n\n\n<h2 id=\"h-understanding-anova-and-post-hoc-testing\">ANOVA ve Post Hoc Testini Anlamak<\/h2>\n\n\n\n<p>ANOVA ve post hoc testinin i\u00e7 i\u00e7e ge\u00e7mi\u015f kavramlar\u0131n\u0131 incelerken, bunlar\u0131 do\u011fru analiz aray\u0131\u015f\u0131nda ortaklar olarak d\u00fc\u015f\u00fcn\u00fcn. Ortalama de\u011ferlerin \u00f6tesine bakmam\u0131z\u0131 ve \u00e7oklu grup kar\u015f\u0131la\u015ft\u0131rmalar\u0131 aras\u0131ndaki daha derin n\u00fcanslar\u0131 ke\u015ffetmemizi sa\u011flarlar - ancak ad\u0131m ad\u0131m ilerleyelim.<\/p>\n\n\n\n<p>\u0130lgili makale: <a href=\"https:\/\/mindthegraph.com\/blog\/post-hoc-analysis\/\"><strong>Post Hoc Analizi: S\u00fcre\u00e7 ve Test T\u00fcrleri<\/strong><\/a><\/p>\n\n\n\n<h3 id=\"h-introduction-to-anova-and-its-purpose-in-statistical-analysis\">ANOVA'ya giri\u015f ve istatistiksel analizdeki amac\u0131<\/h3>\n\n\n\n<p>Varyans Analizi veya istatistik\u00e7iler aras\u0131nda yayg\u0131n olarak bilinen ad\u0131yla ANOVA, cephaneliklerindeki en g\u00fc\u00e7l\u00fc ara\u00e7lardan biri olarak dimdik ayakta durmaktad\u0131r. \u00dc\u00e7 veya daha fazla grup i\u00e7eren bir deneyde grup ortalamalar\u0131 aras\u0131nda istatistiksel olarak anlaml\u0131 farkl\u0131l\u0131klar olup olmad\u0131\u011f\u0131n\u0131 ay\u0131rt etmek gibi kritik bir i\u015flevi vard\u0131r. ANOVA, tek tek gruplar i\u00e7indeki varyanslar\u0131 bu gruplar aras\u0131ndaki varyanslarla kar\u015f\u0131la\u015ft\u0131rarak, rastgele \u015fans d\u0131\u015f\u0131nda varyans olmad\u0131\u011f\u0131 y\u00f6n\u00fcndeki bo\u015f hipotezin reddedilmesine veya korunmas\u0131na yard\u0131mc\u0131 olur.<\/p>\n\n\n\n<h3 id=\"h-explanation-of-post-hoc-testing-and-its-importance-in-anova\">Post hoc testinin a\u00e7\u0131klanmas\u0131 ve ANOVA'daki \u00f6nemi<\/h3>\n\n\n\n<p>B\u00fcy\u00fck setler aras\u0131nda anlaml\u0131l\u0131\u011f\u0131 belirlemek \u00f6nemli olsa da, ANOVA bize \"bir \u015feyin\" farkl\u0131 oldu\u011funu s\u00f6yledi\u011finde ancak \"ne\" ve \"nerede\" oldu\u011funu belirtmedi\u011finde ne olur? Post hoc testini i\u015faret edin! \"Bundan sonra\" ifadesinin k\u0131saltmas\u0131 olan post hoc testi, ANOVA'n\u0131n \u00e7ok y\u00f6nl\u00fc testinin b\u0131rakt\u0131\u011f\u0131 izi takip eder. G\u00f6revi nedir? Gruplar\u0131m\u0131z aras\u0131nda hangi \u00e7iftlerin veya kombinasyonlar\u0131n \u00f6nemli farkl\u0131l\u0131klar sergiledi\u011fini tam olarak belirlemek ve b\u00f6ylece ara\u015ft\u0131rmac\u0131lar\u0131n kusursuz bir hassasiyetle bilin\u00e7li kararlar almas\u0131n\u0131 sa\u011flamak.<\/p>\n\n\n\n<h3 id=\"h-overview-of-the-process-of-post-hoc-testing-in-anova\">ANOVA'da post hoc test s\u00fcrecine genel bak\u0131\u015f<\/h3>\n\n\n\n<p>Post hoc testi ile u\u011fra\u015fmak her zaman bir ANOVA omnibus testinden anlaml\u0131 bir sonu\u00e7 elde ettikten sonra gelir - geriye d\u00f6n\u00fck ad\u0131 da buradan gelir. Bu s\u00fcrecin b\u00fcy\u00fck \u00f6l\u00e7\u00fcde \u015funlardan olu\u015ftu\u011funu hayal edin:<\/p>\n\n\n\n<ul>\n<li><strong>Uygun post hoc testinin se\u00e7ilmesi<\/strong>: Tasar\u0131m \u00f6zelliklerine ve hata oran\u0131 tolerans\u0131na ba\u011fl\u0131 olarak.<\/li>\n\n\n\n<li><strong>P-de\u011ferlerinin ayarlanmas\u0131<\/strong>: \u00c7oklu kar\u015f\u0131la\u015ft\u0131rmalarla ili\u015fkili \u015fi\u015firilmi\u015f risklerin d\u00fczeltilmesi.<\/li>\n\n\n\n<li><strong>Sonu\u00e7lar\u0131n ba\u011flam i\u00e7inde yorumlanmas\u0131<\/strong>: Pratik anlaml\u0131l\u0131\u011f\u0131n istatistiksel bulgularla uyumlu olmas\u0131n\u0131 sa\u011flamak.<\/li>\n<\/ul>\n\n\n\n<p>Bu disiplinli yakla\u015f\u0131m, sahte sonu\u00e7lara kar\u015f\u0131 koruma sa\u011flarken veri k\u00fcmeleri i\u00e7inde uyuyan de\u011ferli i\u00e7g\u00f6r\u00fcleri ortaya \u00e7\u0131kar\u0131r. Bu geli\u015fmi\u015f ancak eri\u015filebilir anlay\u0131\u015fla donanm\u0131\u015f olmak, herkesi veri anlat\u0131lar\u0131 \u00fczerinde ustala\u015fmaya do\u011fru bir yola sokabilir.<\/p>\n\n\n\n<h2 id=\"h-anova-omnibus-test\">ANOVA Omnibus Testi<\/h2>\n\n\n\n<p>En az birinin di\u011ferlerinden farkl\u0131 olup olmad\u0131\u011f\u0131n\u0131 anlamak i\u00e7in ikiden fazla ortalamaya sahip veri setlerini analiz etmek, Varyans Analizinin (ANOVA) gerekli hale geldi\u011fi yerdir. Ancak ANOVA'da post hoc testinin inceliklerine dalmadan \u00f6nce, temel de\u011ferlendirme olan ANOVA omnibus testini kavramak \u00e7ok \u00f6nemlidir. Bunu, ilk kan\u0131tlar\u0131n bir \u015f\u00fcpheli olas\u0131l\u0131\u011f\u0131na i\u015faret etti\u011fi ancak tam olarak kim oldu\u011funu belirlemedi\u011fi bir dedektif hikayesi olarak d\u00fc\u015f\u00fcn\u00fcn.<\/p>\n\n\n\n<p>\u0130lgili makale: <a href=\"https:\/\/mindthegraph.com\/blog\/one-way-anova\/\"><strong>Tek Y\u00f6nl\u00fc ANOVA: Anlama, Y\u00fcr\u00fctme ve Sunma<\/strong><\/a><\/p>\n\n\n\n<h3 id=\"h-detailed-explanation-of-the-anova-omnibus-test\">ANOVA omnibus testinin detayl\u0131 a\u00e7\u0131klamas\u0131<\/h3>\n\n\n\n<p>ANOVA omnibus testi, her olas\u0131 \u00e7iftin her bir anlaml\u0131l\u0131k d\u00fczeyi i\u00e7in \u00e7ok say\u0131da test yapmak yerine birden fazla grup ortalamas\u0131n\u0131 ayn\u0131 anda kar\u015f\u0131la\u015ft\u0131rmam\u0131za izin verdi\u011fi i\u00e7in \u00f6ne \u00e7\u0131kmaktad\u0131r, bu da \u015f\u00fcphesiz tip I hata risklerini (yanl\u0131\u015f pozitif oran\u0131) art\u0131racakt\u0131r. Ad\u0131ndaki \"omnibus\", bu testin genel bir bak\u0131\u015f a\u00e7\u0131s\u0131na sahip oldu\u011funu g\u00f6stermektedir - grup ortalamalar\u0131 aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 toplu olarak kontrol etmektedir.<\/p>\n\n\n\n<p>\u015e\u00f6yle geli\u015fiyor: Grup i\u00e7i ve gruplar aras\u0131 ayr\u0131 varyanslar\u0131 hesaplayarak ba\u015flar\u0131z. E\u011fer gruplar\u0131m\u0131z i\u00e7sel olarak olduk\u00e7a homojen ancak birbirlerinden b\u00fcy\u00fck \u00f6l\u00e7\u00fcde farkl\u0131ysa, bu t\u00fcm grup ortalamalar\u0131n\u0131n e\u015fit olmad\u0131\u011f\u0131n\u0131n sa\u011flam bir g\u00f6stergesidir. Esasen, rastgele dalgalanmalardan bekledi\u011fimiz grup i\u00e7i de\u011fi\u015fkenli\u011fe k\u0131yasla sadece \u015fansla a\u00e7\u0131klanamayan gruplar aras\u0131 b grup i\u00e7i de\u011fi\u015fkenli\u011fi ar\u0131yoruz.<\/p>\n\n\n\n<h3 id=\"h-understanding-the-f-statistic-and-its-interpretation\">F-istatisti\u011finin anla\u015f\u0131lmas\u0131 ve yorumlanmas\u0131<\/h3>\n\n\n\n<p>Bir ANOVA omnibus testi ger\u00e7ekle\u015ftirirken, gruplar aras\u0131 varyans\u0131n grup i\u00e7i varyansa b\u00f6l\u00fcnmesiyle elde edilen bir de\u011fer olan F-istatisti\u011fini hesaplar\u0131z. B\u00fcy\u00fck bir F-de\u011feri grup ortalamalar\u0131 aras\u0131nda \u00f6nemli farkl\u0131l\u0131klar oldu\u011funu g\u00f6sterebilir \u00e7\u00fcnk\u00fc gruplar aras\u0131 de\u011fi\u015fkenli\u011fin grup i\u00e7i de\u011fi\u015fkenli\u011fe k\u0131yasla daha y\u00fcksek oldu\u011funu g\u00f6sterir.<\/p>\n\n\n\n<p>Ancak burada dikkatli olmak \u00e7ok \u00f6nemlidir: F-istatisti\u011fi bo\u015f hipotez alt\u0131nda (grup ortalamalar\u0131 aras\u0131nda fark olmad\u0131\u011f\u0131n\u0131 \u00f6ne s\u00fcren) belirli bir da\u011f\u0131l\u0131m izler. Sadece bu istatisti\u011fe dayanarak bir sonuca varmadan \u00f6nce, hem gruplar aras\u0131 hem de grup i\u00e7i serbestlik derecelerimizi dikkate alarak bu F-da\u011f\u0131l\u0131m\u0131na ba\u015fvururuz ve bize bir p-de\u011feri verir.<\/p>\n\n\n\n<h3 id=\"h-interpreting-the-results-of-the-omnibus-test\">Omnibus testinin sonu\u00e7lar\u0131n\u0131n yorumlanmas\u0131<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/images.surferseo.art\/13a9a93f-5e2f-44b6-93cc-f8f1290e4196.jpeg\" alt=\"\"\/><figcaption class=\"wp-element-caption\"><em><strong>Kaynak: <a href=\"https:\/\/pixabay.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Pixabay<\/a><\/strong><\/em><\/figcaption><\/figure><\/div>\n\n\n<p>Analizinizi yapt\u0131n\u0131z ve hesaplad\u0131\u011f\u0131n\u0131z F-istatisti\u011fini uygun da\u011f\u0131l\u0131mla kar\u015f\u0131la\u015ft\u0131rd\u0131ktan sonra o \u00e7ok \u00f6nemli p-de\u011ferini elde ettiniz - peki \u015fimdi ne olacak? E\u011fer bu p-de\u011feri e\u015fik de\u011ferinizin alt\u0131na d\u00fc\u015ferse -genellikle 0,05- s\u0131f\u0131r hipotezimiz i\u00e7in ret b\u00f6lgesine ula\u015f\u0131r\u0131z. Bu, t\u00fcm gruplar aras\u0131nda etki olmad\u0131\u011f\u0131na dair g\u00fc\u00e7l\u00fc kan\u0131tlar oldu\u011funu g\u00f6sterir.<\/p>\n\n\n\n<p>Ancak -ki bu k\u0131s\u0131m \u00e7ok \u00f6nemlidir- genel bir ret bize hangi belirli ortalamalar\u0131n ne kadar farkl\u0131 oldu\u011fu konusunda yol g\u00f6stermez; daha \u00f6nceki dedektif benzetmemizdeki gibi 'kimin yapt\u0131\u011f\u0131n\u0131' belirtmez. Sadece s\u0131ralamam\u0131zda daha fazla ara\u015ft\u0131rmaya de\u011fer bir \u015fey oldu\u011funu bildirir - ki bu da bizi do\u011frudan ANOVA'da post hoc testine g\u00f6t\u00fcr\u00fcr - belirli \u00e7iftler veya grup kombinasyonlar\u0131 aras\u0131ndaki bu ayr\u0131nt\u0131l\u0131 farkl\u0131l\u0131klar\u0131 \u00e7\u00f6zmek i\u00e7in.<\/p>\n\n\n\n<p>Post hoc testlerinin ne zaman ve neden bir ANOVA omnibus testini takip etti\u011fini anlamak, ara\u015ft\u0131rmac\u0131lar\u0131n bulgular\u0131n\u0131 erken veya yanl\u0131\u015f bir \u015fekilde ili\u015fkilendirmelere veya nedensel ifadelere atlamadan sorumlu bir \u015fekilde ele almalar\u0131n\u0131 sa\u011flar ve \u00e7al\u0131\u015fma alanlar\u0131nda a\u00e7\u0131k bir ileti\u015fime yard\u0131mc\u0131 olur.<\/p>\n\n\n\n<h2 id=\"h-need-for-post-hoc-testing-in-anova\">ANOVA'da Post Hoc Testi \u0130htiyac\u0131<\/h2>\n\n\n\n<h3 id=\"h-exploring-the-limitations-of-the-omnibus-test\">Omnibus testinin s\u0131n\u0131rlamalar\u0131n\u0131n ara\u015ft\u0131r\u0131lmas\u0131<\/h3>\n\n\n\n<p>\u0130statistiksel analizin karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 incelerken, Varyans Analizi (ANOVA) gibi ara\u00e7lar\u0131n g\u00fc\u00e7l\u00fc olmalar\u0131na ra\u011fmen s\u0131n\u0131rlar\u0131 oldu\u011funu kabul etmek \u00e7ok \u00f6nemlidir. ANOVA omnibus testi bize gruplar\u0131m\u0131z aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 etkili bir \u015fekilde s\u00f6yler. Ancak, farkl\u0131 \u00f6\u011fretim y\u00f6ntemlerinin \u00f6\u011frenci performans\u0131 \u00fczerindeki etkilerini inceledi\u011finizi varsayal\u0131m. Bu durumda, omnibus testi test edilen t\u00fcm y\u00f6ntemler aras\u0131nda farkl\u0131l\u0131klar ortaya \u00e7\u0131karabilir, ancak bu farkl\u0131l\u0131klar\u0131n nerede yatt\u0131\u011f\u0131n\u0131 belirtmez - hangi \u00f6\u011fretim y\u00f6ntemlerinin \u00e7iftleri veya kombinasyonlar\u0131 birbirinden \u00f6nemli \u00f6l\u00e7\u00fcde farkl\u0131d\u0131r.<\/p>\n\n\n\n<p>\u0130\u015fin \u00f6z\u00fc \u015fudur: ANOVA en az iki grubun farkl\u0131 oldu\u011funu g\u00f6sterse de ayr\u0131nt\u0131lar konusunda sessiz kal\u0131r. Bu, de\u011ferini bilmeden kazanan bir piyango biletine sahip oldu\u011funuzu bilmek gibidir - detaylar i\u00e7in daha derine inmek ister miydiniz?<\/p>\n\n\n\n<h3 id=\"h-understanding-why-post-hoc-tests-are-necessary\">Post hoc testlerin neden gerekli oldu\u011funu anlamak<\/h3>\n\n\n\n<p>Ayr\u0131nt\u0131lara inmek tam da post hoc test ANOVA'n\u0131n devreye girdi\u011fi yerdir. ANOVA genel anlaml\u0131l\u0131\u011fa i\u015faret eden ye\u015fil bir bayrak sallad\u0131\u011f\u0131nda, geriye k\u0131\u015fk\u0131rt\u0131c\u0131 sorular kal\u0131r: Bu farkl\u0131l\u0131klar tam olarak hangi gruplardan kaynaklan\u0131yor? Her grup birbirinden farkl\u0131 m\u0131, yoksa sadece belirli gruplar m\u0131 de\u011fi\u015fimi y\u00f6nlendiriyor?<\/p>\n\n\n\n<p>Bu sorular\u0131 daha fazla de\u011ferlendirme yapmadan yan\u0131tlamaya \u00e7al\u0131\u015fmak, belirli ayr\u0131mlardan ziyade genel e\u011filimlere dayal\u0131 yanl\u0131\u015f sonu\u00e7lara varma riskini ta\u015f\u0131r. Post hoc testler, verileri ayr\u0131\u015ft\u0131ran ve ilk ANOVA'n\u0131z gruplar aras\u0131ndaki geni\u015f varyasyonlara i\u015faret ettikten sonra bireysel grup kar\u015f\u0131la\u015ft\u0131rmalar\u0131 hakk\u0131nda ayr\u0131nt\u0131l\u0131 bilgiler sa\u011flayan ince bir yakla\u015f\u0131mla donat\u0131lm\u0131\u015ft\u0131r.<\/p>\n\n\n\n<p>Bu takip de\u011ferlendirmeleri, hangi z\u0131tl\u0131klar\u0131n \u00f6nemli oldu\u011funu tam olarak belirler ve sonu\u00e7lar\u0131n\u0131z\u0131n incelikli bir \u015fekilde anla\u015f\u0131lmas\u0131n\u0131 sa\u011flarken onlar\u0131 vazge\u00e7ilmez k\u0131lar.<\/p>\n\n\n\n<h3 id=\"h-the-concept-of-experiment-wise-error-rate\">Deneye dayal\u0131 hata oran\u0131 kavram\u0131<\/h3>\n\n\n\n<p>Post hoc testin ne zaman zorunlu oldu\u011funa karar vermede \u00f6nemli bir temel ilke, istatistik\u00e7ilerin \"deneysel hata oran\u0131\" olarak adland\u0131rd\u0131klar\u0131 \u015feyde yatmaktad\u0131r. Bu, bir deneyde ger\u00e7ekle\u015ftirilen t\u00fcm hipotez testleri boyunca en az bir Tip I hata yapma olas\u0131l\u0131\u011f\u0131n\u0131 ifade eder - sadece kar\u015f\u0131la\u015ft\u0131rma ba\u015f\u0131na de\u011fil, t\u00fcm olas\u0131 post hoc ikili kar\u015f\u0131la\u015ft\u0131rma testleri boyunca k\u00fcm\u00fclatif olarak.<\/p>\n\n\n\n<p>Herhangi bir lezzetin daha lezzetli olarak \u00f6ne \u00e7\u0131k\u0131p \u00e7\u0131kmad\u0131\u011f\u0131n\u0131 belirlemeye \u00e7al\u0131\u015f\u0131rken \u00e7e\u015fitli kurabiye gruplar\u0131n\u0131 tatt\u0131\u011f\u0131n\u0131z\u0131 d\u00fc\u015f\u00fcn\u00fcn. Her bir tat testi, bir partiyi sadece \u015fans eseri yanl\u0131\u015fl\u0131kla \u00fcst\u00fcn ilan etme olas\u0131l\u0131\u011f\u0131n\u0131 art\u0131r\u0131r - ne kadar \u00e7ok kar\u015f\u0131la\u015ft\u0131rma yaparsan\u0131z, baz\u0131 bulgular yanl\u0131\u015f alarm olabilece\u011finden yanl\u0131\u015f de\u011ferlendirme riskiniz o kadar y\u00fcksek olur.<\/p>\n\n\n\n<p>Post hoc testi, bu k\u00fcm\u00fclatif hatay\u0131 hesaba katarak ve d\u00fczeltilmi\u015f p-de\u011ferlerini kullanarak kontrol ederek istatistiksel ara\u00e7 setimize karma\u015f\u0131kl\u0131k katar; bu prosed\u00fcr yaln\u0131zca daha fazla do\u011fruluk i\u00e7in de\u011fil, ayn\u0131 zamanda sonu\u00e7lar\u0131m\u0131z\u0131n ge\u00e7erlili\u011fi ve g\u00fcvenilirli\u011fine g\u00fcvenmek i\u00e7in de tasarlanm\u0131\u015ft\u0131r.<\/p>\n\n\n\n<h2 id=\"h-different-post-hoc-testing-methods\">Farkl\u0131 Post-Hoc Test Y\u00f6ntemleri<\/h2>\n\n\n\n<p>Grup ortalamalar\u0131 aras\u0131nda istatistiksel olarak anlaml\u0131 bir etki olup olmad\u0131\u011f\u0131n\u0131 s\u00f6yleyen bir ANOVA ger\u00e7ekle\u015ftirdikten sonra, farkl\u0131l\u0131klar\u0131n ger\u00e7ekte nerede yatt\u0131\u011f\u0131n\u0131 merak etmek olduk\u00e7a yayg\u0131nd\u0131r. \u0130\u015fte bu noktada post hoc testler devreye girer - bunu, her bir karakterin rol\u00fcn\u00fc anlamak i\u00e7in verilerinizin anlat\u0131s\u0131na daha yak\u0131ndan bakmak olarak d\u00fc\u015f\u00fcn\u00fcn. Bu n\u00fcansl\u0131 hikayeleri ayd\u0131nlatan baz\u0131 y\u00f6ntemlerle bu konuyu daha ayr\u0131nt\u0131l\u0131 inceleyelim.<\/p>\n\n\n\n<h3 id=\"h-tukey-s-method\">Tukey Y\u00f6ntemi<\/h3>\n\n\n\n<h4 id=\"h-explanation-of-tukey-s-method-and-its-application-in-anova\">Tukey y\u00f6nteminin a\u00e7\u0131klanmas\u0131 ve ANOVA'da uygulanmas\u0131<\/h4>\n\n\n\n<p><strong>Tukey'in D\u00fcr\u00fcst Anlaml\u0131 Farkl\u0131l\u0131\u011f\u0131 (HSD)<\/strong> y\u00f6ntemi, bir ANOVA'y\u0131 takiben en yayg\u0131n kullan\u0131lan post hoc testlerinden biridir. T\u00fcm grup ortalamalar\u0131n\u0131n e\u015fit olmad\u0131\u011f\u0131n\u0131 fark etti\u011finizde, ancak hangi belirli ortalamalar\u0131n farkl\u0131 oldu\u011funu bilmeniz gerekti\u011finde, Tukey'in y\u00f6ntemi devreye girer. Bu kar\u015f\u0131la\u015ft\u0131rmalarda Tip I hata oran\u0131n\u0131 kontrol ederken olas\u0131 t\u00fcm ortalama \u00e7iftlerini kar\u015f\u0131la\u015ft\u0131r\u0131r. Bu \u00f6zellik, birden fazla grupla \u00e7al\u0131\u015f\u0131yorsan\u0131z ve sa\u011flam bir analiz i\u00e7in \u00e7oklu kar\u015f\u0131la\u015ft\u0131rma testlerine ihtiya\u00e7 duyuyorsan\u0131z bu y\u00f6ntemi \u00f6zellikle kullan\u0131\u015fl\u0131 hale getirir.<\/p>\n\n\n\n<h4 id=\"h-calculation-and-interpretation-of-adjusted-p-values\">D\u00fczeltilmi\u015f p-de\u011ferlerinin hesaplanmas\u0131 ve yorumlanmas\u0131<\/h4>\n\n\n\n<p>Tukey'in y\u00f6ntemi, grup ortalamalar\u0131 aras\u0131ndaki her ikili kar\u015f\u0131la\u015ft\u0131rma i\u00e7in bir dizi \"d\u00fczeltilmi\u015f\" p-de\u011ferinin hesaplanmas\u0131n\u0131 i\u00e7erir. Hesaplama, hem grup i\u00e7i hem de gruplar aras\u0131 varyanslar\u0131 hesaba katan \u00f6\u011frencile\u015ftirilmi\u015f aral\u0131k da\u011f\u0131l\u0131m\u0131ndan yararlan\u0131r - hepsi olduk\u00e7a kafa kar\u0131\u015ft\u0131r\u0131c\u0131d\u0131r ancak verilerinizdeki n\u00fcanslar\u0131 yorumlamak i\u00e7in merkezidir. \u00d6nemli olan, bu p-de\u011ferlerini \u00e7oklu kar\u015f\u0131la\u015ft\u0131rmalar nedeniyle artan Tip I hata potansiyelini hesaba katacak \u015fekilde ayarlaman\u0131zd\u0131r. Belirli bir d\u00fczeltilmi\u015f p-de\u011feri anlaml\u0131l\u0131k e\u015fi\u011finin (genellikle 0,05) alt\u0131na d\u00fc\u015ferse, i\u015fte o zaman bu iki grup ortalamas\u0131 aras\u0131nda anlaml\u0131 bir fark oldu\u011funu ilan edebilirsiniz.<\/p>\n\n\n\n<h4 id=\"h-using-simultaneous-confidence-intervals-with-tukey-s-method\">Tukey y\u00f6ntemi ile e\u015fzamanl\u0131 g\u00fcven aral\u0131klar\u0131n\u0131n kullan\u0131lmas\u0131<\/h4>\n\n\n\n<p>Tukey testinin bir di\u011fer g\u00fc\u00e7l\u00fc y\u00f6n\u00fc de t\u00fcm ortalama farkl\u0131l\u0131klar i\u00e7in e\u015f zamanl\u0131 g\u00fcven aral\u0131klar\u0131 olu\u015fturabilmesidir. Ortalama fark\u0131n bu g\u00f6rsel temsili, ara\u015ft\u0131rmac\u0131lar\u0131n yaln\u0131zca hangi gruplar\u0131n farkl\u0131 oldu\u011funu g\u00f6rmelerine de\u011fil, ayn\u0131 zamanda bu farkl\u0131l\u0131klar\u0131n b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fc ve y\u00f6n\u00fcn\u00fc anlamalar\u0131na da yard\u0131mc\u0131 olur - gelecekteki ara\u015ft\u0131rmalar\u0131 veya pratik uygulamalar\u0131 \u00e7izerken paha bi\u00e7ilmez bir i\u00e7g\u00f6r\u00fc.<\/p>\n\n\n\n<h3 id=\"h-holm-s-method\">Holm'un Y\u00f6ntemi<\/h3>\n\n\n\n<h4 id=\"h-introduction-to-holm-s-method-and-its-advantages-over-other-methods\">Holm y\u00f6ntemine giri\u015f ve di\u011fer y\u00f6ntemlere g\u00f6re avantajlar\u0131<\/h4>\n\n\n\n<p>Vites de\u011fi\u015ftiriyorum, <strong>Holm'un y\u00f6ntemi<\/strong>Holm'un s\u0131ral\u0131 Bonferroni prosed\u00fcr\u00fc olarak da bilinen bu y\u00f6ntem, I. Tip hatalara kar\u015f\u0131 koruman\u0131n \u00f6n planda oldu\u011fu alternatif bir post hoc test y\u00f6ntemi sunar; p-de\u011ferlerini, de\u011ferli eserleri gereksiz te\u015fhirden koruyan dikkatli bir k\u00fcrat\u00f6r gibi ayarlar. En \u015fa\u015f\u0131rt\u0131c\u0131 avantaj\u0131 prosed\u00fcrel esneklikte yatmaktad\u0131r; tek ad\u0131ml\u0131 ayarlamalar\u0131 sabitleyen baz\u0131 y\u00f6ntemlerin aksine, Holm'un ad\u0131m ad\u0131m yakla\u015f\u0131m\u0131 daha fazla g\u00fc\u00e7 sunarken, bir\u00e7ok kar\u015f\u0131la\u015ft\u0131rmadan kaynaklanan istatistiksel hatalara kar\u015f\u0131 da savunma yapmaktad\u0131r.<\/p>\n\n\n\n<h4 id=\"h-calculation-and-interpretation-of-adjusted-p-values-with-holm-s-method\">Holm y\u00f6ntemi ile d\u00fczeltilmi\u015f p-de\u011ferlerinin hesaplanmas\u0131 ve yorumlanmas\u0131<\/h4>\n\n\n\n<p>\u0130\u015fin \u00f6z\u00fc, ba\u015flang\u0131\u00e7taki d\u00fczeltilmemi\u015f p-de\u011ferlerimizi en k\u00fc\u00e7\u00fckten en b\u00fcy\u00fc\u011fe do\u011fru s\u0131ralamay\u0131 ve bunlar\u0131 s\u0131ralama konumlar\u0131na g\u00f6re de\u011fi\u015ftirilmi\u015f alfa seviyelerine kar\u015f\u0131 s\u0131ral\u0131 incelemeye tabi tutmay\u0131 i\u00e7erir - hesaplanan e\u015fi\u011fimizden inatla daha b\u00fcy\u00fck bir de\u011fere ula\u015fana kadar bir t\u00fcr 'a\u015fa\u011f\u0131 inme' s\u00fcreci; ipu\u00e7lar\u0131 bu noktadan sonra \u00e7\u0131kar\u0131l\u0131r.<\/p>\n\n\n\n<h3 id=\"h-dunnett-s-method\">Dunnett'in Y\u00f6ntemi<\/h3>\n\n\n\n<h4 id=\"h-explanation-of-dunnett-s-method-and-when-it-is-appropriate-to-use-it\">Dunnett y\u00f6nteminin a\u00e7\u0131klanmas\u0131 ve ne zaman kullan\u0131lmas\u0131n\u0131n uygun oldu\u011fu<\/h4>\n\n\n\n<p>\u0130\u015fte burada <strong>Dunnett'in testi<\/strong>Hedefe y\u00f6nelik yakla\u015f\u0131m\u0131 ile ay\u0131rt edilir: birden fazla tedavi grubunu \u00f6zellikle tek bir kontrol grubuyla kar\u015f\u0131la\u015ft\u0131rmak; bu, yeni tedavileri bir standart veya plasebo kar\u015f\u0131la\u015ft\u0131rma \u00f6l\u00e7\u00fct\u00fcne g\u00f6re tartmak isteyebilece\u011finiz klinik deneylerde veya tar\u0131msal \u00e7al\u0131\u015fmalarda yayg\u0131n bir senaryodur.<\/p>\n\n\n\n<h4 id=\"h-comparing-treatment-groups-to-a-control-group-using-dunnett-s-method\">Dunnett y\u00f6ntemini kullanarak tedavi gruplar\u0131n\u0131 bir kontrol grubuyla kar\u015f\u0131la\u015ft\u0131rma<\/h4>\n\n\n\n<p>T\u00fcm olas\u0131 kar\u015f\u0131la\u015ft\u0131rmalara daha geni\u015f a\u011flar atan di\u011fer yakla\u015f\u0131mlar\u0131n aksine, Dunnett'in se\u00e7ici g\u00f6zleri yaln\u0131zca her aday\u0131n se\u00e7ti\u011fimiz referans noktas\u0131n\u0131n yan\u0131nda nas\u0131l durdu\u011funa bakar. Bu nedenle, hi\u00e7bir \u015fey yapmamak ya da \u015fimdiye kadar denenmi\u015f ve do\u011fru olana ba\u011fl\u0131 kalmak yerine m\u00fcdahalelerinizden ne kadar daha fazla kald\u0131ra\u00e7 elde etti\u011fimizi -ya da etmedi\u011fimizi- dikkatlice hesaplar.<\/p>\n\n\n\n<p>ANOVA'daki bu \u00e7e\u015fitli post hoc test ara\u00e7lar\u0131, biz istatistik\u00e7ilerin ve veri analistlerinin, say\u0131sal y\u00fczeylerinin alt\u0131nda bekleyen potansiyel i\u00e7g\u00f6r\u00fclerle dolu veri k\u00fcmelerinden ayr\u0131nt\u0131lar \u00e7\u0131karmam\u0131za olanak tan\u0131r - her biri ampirik ara\u015ft\u0131rmalar\u0131m\u0131z\u0131 olu\u015fturan dokuya dokunmu\u015f gizli hikayeleri ortaya \u00e7\u0131karmak i\u00e7in biraz farkl\u0131 \u015fekilde uyarlanm\u0131\u015ft\u0131r.<\/p>\n\n\n\n<h2 id=\"h-factors-to-consider-in-choosing-a-post-hoc-test\">Post-hoc Testi Se\u00e7iminde Dikkate Al\u0131nmas\u0131 Gereken Fakt\u00f6rler<\/h2>\n\n\n\n<p>ANOVA alan\u0131na girdi\u011finizde, bir omnibus ANOVA testi kullanarak gruplar aras\u0131nda \u00f6nemli bir fark belirledikten sonra, bir sonraki ad\u0131m genellikle bu farkl\u0131l\u0131klar\u0131n tam olarak nerede oldu\u011funu belirlemek i\u00e7in post hoc testi kullanmakt\u0131r. \u015eimdi, hangi post hoc testini se\u00e7ece\u011finizi etkilemesi gereken kritik fakt\u00f6rlerden biri konusunda size rehberlik etmeme izin verin: aile baz\u0131nda hata oran\u0131 kontrol\u00fc.<\/p>\n\n\n\n<h3 id=\"h-famil-wise-error-rate-control-and-its-significance-in-choosing-a-test-method\">Ailesel Hata Oran\u0131 Kontrol\u00fc ve Test Y\u00f6ntemi Se\u00e7imindeki \u00d6nemi<\/h3>\n\n\n\n<p>'Family-wise error rate' (FWER) terimi, \u00e7oklu ikili testler yaparken t\u00fcm olas\u0131 kar\u015f\u0131la\u015ft\u0131rmalar aras\u0131nda en az bir Tip I hata yapma olas\u0131l\u0131\u011f\u0131n\u0131 ifade eder. Tip I hata, ger\u00e7ekte olmad\u0131\u011f\u0131 halde gruplar aras\u0131nda farkl\u0131l\u0131klar oldu\u011fu sonucuna yanl\u0131\u015f bir \u015fekilde vard\u0131\u011f\u0131n\u0131zda ortaya \u00e7\u0131kar. Uygun \u015fekilde kontrol edilmezse, ANOVA \u00e7er\u00e7evemizde giderek daha fazla \u00e7oklu ikili kar\u015f\u0131la\u015ft\u0131rma yapt\u0131k\u00e7a, yanl\u0131\u015fl\u0131kla yanl\u0131\u015f bir anlaml\u0131l\u0131k balonu ilan etme olas\u0131l\u0131\u011f\u0131 artar - potansiyel olarak \u00e7al\u0131\u015fman\u0131z\u0131 yanl\u0131\u015f y\u00f6nlendirir.<\/p>\n\n\n\n<p>Bu kula\u011fa \u00fcrk\u00fct\u00fcc\u00fc gelse de korkmay\u0131n; FWER kontrol y\u00f6ntemlerinin post hoc test se\u00e7iminde \u00e7ok \u00f6nemli bir unsur olmas\u0131n\u0131n nedeni de budur. Esasen bu y\u00f6ntemler, anlaml\u0131l\u0131k e\u015fiklerinizi veya p-de\u011ferlerinizi, t\u00fcm testlerdeki kolektif risk, hatalar i\u00e7in orijinal kabul seviyenizi (genellikle 0,05) a\u015fmayacak \u015fekilde ayarlar. Bunu yaparak, yanl\u0131\u015f ke\u015fif \u015fans\u0131m\u0131z\u0131 art\u0131rmadan belirli grup farkl\u0131l\u0131klar\u0131n\u0131 g\u00fcvenle ke\u015ffedebiliriz.<\/p>\n\n\n\n<p>FWER'in kontrol edilmesi, bulgular\u0131n\u0131z\u0131n b\u00fct\u00fcnl\u00fc\u011f\u00fcn\u00fc korur ve akran de\u011ferlendirmesi ve tekrarlanabilirlik i\u00e7in gerekli olan bilimsel titizli\u011fi s\u00fcrd\u00fcr\u00fcr.<\/p>\n\n\n\n<p>\u015eimdi \u00e7e\u015fitli post hoc test se\u00e7enekleriyle kar\u015f\u0131 kar\u015f\u0131ya oldu\u011funuzu d\u00fc\u015f\u00fcn\u00fcn - FWER'i anlamak temel sorular\u0131 yan\u0131tlaman\u0131za yard\u0131mc\u0131 olur:<\/p>\n\n\n\n<ul>\n<li>\u00c7al\u0131\u015fma tasar\u0131m\u0131mda ka\u00e7 kar\u015f\u0131la\u015ft\u0131rma yap\u0131lacak?<\/li>\n\n\n\n<li>Alan\u0131m\u0131 veya ara\u015ft\u0131rma sorumu g\u00f6z \u00f6n\u00fcnde bulundurarak I. Tip hatalar\u0131 kontrol etmede ne kadar muhafazakar olmam gerekir?<\/li>\n<\/ul>\n\n\n\n<p>\u00d6rne\u011fin, Tukey HSD (D\u00fcr\u00fcst\u00e7e Anlaml\u0131 Fark), m\u00fcmk\u00fcn olan t\u00fcm ikili kar\u015f\u0131la\u015ft\u0131rmalar\u0131 ve k\u0131yaslamalar\u0131 yapt\u0131\u011f\u0131m\u0131zda ve aile baz\u0131nda hata oran\u0131m\u0131z\u0131 alfa seviyemize (genellikle 0,05) e\u015fit tutmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131zda en uygun y\u00f6ntemdir. Holm'un y\u00f6ntemi, p-de\u011ferlerini s\u0131rayla ayarlayarak ve bir denge kurarak bir ad\u0131m \u00f6ne \u00e7\u0131kar - Bonferroni'den daha az muhafazakard\u0131r ancak yine de Tip I hatalara kar\u015f\u0131 makul bir koruma sa\u011flar. Peki ya tasar\u0131m\u0131n\u0131zda tek bir kontrol veya referans grubu varsa? Dunnett'in y\u00f6ntemi devreye girebilir \u00e7\u00fcnk\u00fc \u00f6zellikle bu merkezi fig\u00fcrle kar\u015f\u0131la\u015ft\u0131rmalar\u0131 ele al\u0131r.<\/p>\n\n\n\n<p>Sonu\u00e7 olarak:<\/p>\n\n\n\n<p>Artan hipotez testleriyle ili\u015fkili risklerin etkili bir \u015fekilde azalt\u0131lmas\u0131, istatistiksel analiz y\u00f6ntemleriyle ilgili ak\u0131ll\u0131 se\u00e7imler yap\u0131lmas\u0131n\u0131 gerektirir. Gruplar aras\u0131nda anlaml\u0131 varyans oldu\u011funu g\u00f6steren bir ANOVA sonucunun ard\u0131ndan post hoc testine dalarken her zaman hat\u0131rlay\u0131n: Ailesel hata oran\u0131 kontrol\u00fc sadece istatistiksel bir jargon de\u011fildir; karma\u015f\u0131k veri modellerinden \u00e7\u0131kar\u0131lan sonu\u00e7lar\u0131n g\u00fcvenilirli\u011fini ve ge\u00e7erlili\u011fini sa\u011flayan g\u00fcvencenizdir.<\/p>\n\n\n\n<h2 id=\"h-case-studies-and-examples\">Vaka \u00c7al\u0131\u015fmalar\u0131 ve \u00d6rnekler<\/h2>\n\n\n\n<p>\u0130statistikteki kavramlar\u0131n anla\u015f\u0131lmas\u0131, ger\u00e7ek d\u00fcnya uygulamalar\u0131n\u0131n incelenmesiyle b\u00fcy\u00fck \u00f6l\u00e7\u00fcde geli\u015ftirilir. Post hoc test ANOVA'n\u0131n ara\u015ft\u0131rma \u00e7al\u0131\u015fmalar\u0131na nas\u0131l hayat verdi\u011fini ve bilimsel sorgulamalara bulgular\u0131n\u0131 ke\u015ffetmek i\u00e7in titiz bir y\u00f6ntem kazand\u0131rd\u0131\u011f\u0131n\u0131 inceleyelim.<\/p>\n\n\n\n<h3 id=\"h-discussion-of-real-world-research-studies-where-post-hoc-testing-was-used\">Post hoc testinin kullan\u0131ld\u0131\u011f\u0131 ger\u00e7ek d\u00fcnya ara\u015ft\u0131rma \u00e7al\u0131\u015fmalar\u0131n\u0131n tart\u0131\u015f\u0131lmas\u0131<\/h3>\n\n\n\n<p>Pratik uygulama merce\u011finden bak\u0131ld\u0131\u011f\u0131nda, post hoc analizler ve testler soyut matematiksel prosed\u00fcrlerden daha fazlas\u0131 haline gelir; bunlar veri i\u00e7indeki anlat\u0131lar\u0131 ortaya \u00e7\u0131karan ara\u00e7lard\u0131r. \u00d6rne\u011fin, farkl\u0131 \u00f6\u011fretim metodolojilerinin etkilili\u011fine odaklanan bir \u00e7al\u0131\u015fmada, \u00f6\u011fretim yakla\u015f\u0131m\u0131na ba\u011fl\u0131 olarak \u00f6\u011frenci \u00e7\u0131kt\u0131lar\u0131nda \u00f6nemli farkl\u0131l\u0131klar olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in bir ANOVA kullan\u0131labilir. Omnibus testi anlaml\u0131 bir sonu\u00e7 verirse, hangi y\u00f6ntemlerin birbirinden farkl\u0131 oldu\u011funu tam olarak belirlemek i\u00e7in gerekli olan post hoc analizinin yolunu a\u00e7ar.<\/p>\n\n\n\n<p>Bu metodolojiyi vurgulayan ba\u015fka bir \u00f6rnek payla\u015fay\u0131m: ara\u015ft\u0131rmac\u0131lar\u0131n yeni bir ilac\u0131n kan bas\u0131nc\u0131 seviyeleri \u00fczerindeki etkisini de\u011ferlendiren bir deneyin post hoc analizini yapt\u0131klar\u0131n\u0131 d\u00fc\u015f\u00fcn\u00fcn. \u0130lk ANOVA, kan bas\u0131nc\u0131 de\u011ferlerinin zaman i\u00e7inde farkl\u0131 dozaj gruplar\u0131 aras\u0131nda \u00f6nemli \u00f6l\u00e7\u00fcde de\u011fi\u015fti\u011fini g\u00f6stermektedir. Post hoc testi bir sonraki \u00f6nemli ad\u0131m olarak devreye girerek, bilim insanlar\u0131n\u0131n \u00f6zellikle hangilerinin etkili veya potansiyel olarak zararl\u0131 oldu\u011funu anlamak i\u00e7in m\u00fcmk\u00fcn olan her dozaj \u00e7iftini kar\u015f\u0131la\u015ft\u0131rmas\u0131na yard\u0131mc\u0131 olur.<\/p>\n\n\n\n<p>Bu \u00f6rnekler, ANOVA'dan sonra yap\u0131lan post hoc testlerinin ara\u015ft\u0131rmac\u0131lara ke\u015fif yolculuklar\u0131nda rehberlik etmekle kalmay\u0131p ayn\u0131 zamanda sonu\u00e7lar\u0131nda sa\u011flaml\u0131k ve kesinlik sa\u011flad\u0131\u011f\u0131n\u0131 g\u00f6stermektedir.<\/p>\n\n\n\n<h3 id=\"h-hands-on-examples-illustrating-the-application-of-different-post-hoc-tests\">Farkl\u0131 post hoc testlerinin uygulanmas\u0131n\u0131 g\u00f6steren uygulamal\u0131 \u00f6rnekler<\/h3>\n\n\n\n<p>Belirli uygulamalar i\u00e7in \u00e7oklu kar\u015f\u0131la\u015ft\u0131rma testlerini derinlemesine incelemek, bu testlerin ne kadar \u00e7e\u015fitli olabilece\u011fi konusunda fikir verebilir:<\/p>\n\n\n\n<ul>\n<li><strong>Tukey Y\u00f6ntemi<\/strong>: Tar\u0131m bilimcilerinin birden fazla g\u00fcbre \u00e7e\u015fidi aras\u0131nda mahsul verimini kar\u015f\u0131la\u015ft\u0131rd\u0131klar\u0131n\u0131 d\u00fc\u015f\u00fcn\u00fcn. Uygulamalar aras\u0131nda farkl\u0131 verimler bulan \u00f6nemli bir ANOVA'n\u0131n ard\u0131ndan, Tukey'in y\u00f6ntemi, t\u00fcm kar\u015f\u0131la\u015ft\u0131rmalarda I tipi hatay\u0131 kontrol ederken, hangi g\u00fcbrelerin di\u011ferlerine k\u0131yasla istatistiksel olarak farkl\u0131 mahsuller verdi\u011fini tam olarak ortaya \u00e7\u0131karabilir.<\/li>\n\n\n\n<li><strong>Holm'un Y\u00f6ntemi<\/strong>: Terapi sonu\u00e7lar\u0131n\u0131 anlamay\u0131 ama\u00e7layan psikolojik ara\u015ft\u0131rmalarda, Holm'un s\u0131ral\u0131 prosed\u00fcr\u00fc, birden fazla tedavi formu kontrol gruplar\u0131na kar\u015f\u0131 de\u011ferlendirildi\u011finde p-de\u011ferlerini ayarlayacakt\u0131r. Bu, belirli tedavilerin hi\u00e7bir tedaviden daha iyi performans g\u00f6stermedi\u011fini ke\u015ffettikten sonra bile sonraki bulgular\u0131n g\u00fcvenilir kalmas\u0131n\u0131 sa\u011flar.<\/li>\n\n\n\n<li><strong>Dunnett'in Y\u00f6ntemi<\/strong>: Genellikle plasebo grubu olan klinik \u00e7al\u0131\u015fmalarda kullan\u0131lan Dunnett y\u00f6ntemi, her bir tedaviyi do\u011frudan plasebo ile kar\u015f\u0131la\u015ft\u0131r\u0131r. Plaseboya k\u0131yasla birka\u00e7 yeni a\u011fr\u0131 kesici ilac\u0131 de\u011ferlendiren bir \u00e7al\u0131\u015fma, \u00e7oklu kar\u015f\u0131la\u015ft\u0131rmalar nedeniyle yanl\u0131\u015f pozitif riskini \u015fi\u015firmeden herhangi bir yeni ilac\u0131n \u00fcst\u00fcn bir etkiye sahip olup olmad\u0131\u011f\u0131n\u0131 anlamak i\u00e7in Dunnett'i kullanabilir.<\/li>\n<\/ul>\n\n\n\n<p>Farkl\u0131 alanlardan al\u0131nan bu kesitler, ANOVA'daki \u00f6zel post hoc testinin, anlaml\u0131l\u0131\u011f\u0131n daha d\u00fc\u015f\u00fck istatistiksel g\u00fcc\u00fcne nas\u0131l i\u00e7erik kazand\u0131rd\u0131\u011f\u0131n\u0131n alt\u0131n\u0131 \u00e7iziyor - say\u0131lar\u0131, end\u00fcstrileri \u015fekillendirmeye ve ya\u015famlar\u0131 iyile\u015ftirmeye yard\u0131mc\u0131 olabilecek anlaml\u0131 i\u00e7g\u00f6r\u00fclere d\u00f6n\u00fc\u015ft\u00fcr\u00fcyor.<\/p>\n\n\n\n<h2 id=\"h-statistical-power-in-post-hoc-testing\">Post-Hoc Testlerinde \u0130statistiksel G\u00fc\u00e7<\/h2>\n\n\n\n<h3 id=\"h-explanation-of-statistical-power-and-its-importance-in-post-hoc-testing-decision-making\">\u0130statistiksel g\u00fcc\u00fcn a\u00e7\u0131klanmas\u0131 ve post hoc test karar verme s\u00fcrecindeki \u00f6nemi<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/images.surferseo.art\/290f22f3-906a-4d32-bf9f-a332b21fa8bb.jpeg\" alt=\"\"\/><figcaption class=\"wp-element-caption\"><em><strong>Kaynak: <a href=\"https:\/\/pixabay.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Pixabay<\/a><\/strong><\/em><\/figcaption><\/figure><\/div>\n\n\n<p>ANOVA sonu\u00e7lar\u0131n\u0131 post hoc test etmenin inceliklerini tart\u0131\u015f\u0131rken, hipotez testinin merkezinde yer alan bir kavram\u0131 anlamak zorunludur-istatistiksel g\u00fc\u00e7. Daha basit bir ifadeyle, istatistiksel g\u00fc\u00e7, bir \u00e7al\u0131\u015fman\u0131n ger\u00e7ekten bir etki oldu\u011funda bunu tespit etme olas\u0131l\u0131\u011f\u0131d\u0131r. Bu, e\u011fer ger\u00e7ekten varsa gruplar aras\u0131ndaki ger\u00e7ek farkl\u0131l\u0131klar\u0131 bulmak anlam\u0131na gelir.<\/p>\n\n\n\n<p>Y\u00fcksek istatistiksel g\u00fc\u00e7, ger\u00e7ekte var olan bir fark\u0131 tespit edemedi\u011fimizde ortaya \u00e7\u0131kan Tip II hata yapma olas\u0131l\u0131\u011f\u0131n\u0131 azalt\u0131r. Sonu\u00e7lar\u0131m\u0131z\u0131 yanl\u0131\u015f negatiflere kar\u015f\u0131 koruyarak analizimizden \u00e7\u0131kar\u0131lan sonu\u00e7lar\u0131n g\u00fcvenilirli\u011fini art\u0131r\u0131r. Bu fakt\u00f6r, \u00f6zellikle ANOVA'n\u0131n gruplar aras\u0131nda \u00f6nemli farkl\u0131l\u0131klar oldu\u011funu g\u00f6stermesinin ard\u0131ndan yap\u0131lan post hoc testleri s\u0131ras\u0131nda kritik hale gelir.<\/p>\n\n\n\n<p>Pratik ortamlarda, y\u00fcksek istatistiksel g\u00fcce ula\u015fmak genellikle \u00e7al\u0131\u015fman\u0131z\u0131n yeterli bir \u00f6rneklem b\u00fcy\u00fckl\u00fc\u011f\u00fcne sahip olmas\u0131n\u0131 sa\u011flamak anlam\u0131na gelir. \u00c7ok k\u00fc\u00e7\u00fck bir \u00f6rneklem ger\u00e7ek grup farkl\u0131l\u0131klar\u0131n\u0131 do\u011fru bir \u015fekilde yans\u0131tmayabilirken, istisnai derecede b\u00fcy\u00fck \u00f6rneklemler istatistiksel olarak anlaml\u0131 ancak pratikte \u00f6nemsiz farkl\u0131l\u0131klar\u0131 ortaya \u00e7\u0131karabilir. Bundan b\u00f6yle, bu hususlar\u0131 dengelemek, post hoc test ANOVA i\u00e7eren herhangi bir ara\u015ft\u0131rma ortam\u0131nda mant\u0131kl\u0131 karar vermek i\u00e7in \u00e7ok \u00f6nemlidir.<\/p>\n\n\n\n<h3 id=\"h-managing-power-trade-offs-by-reducing-the-number-of-comparisons\">Kar\u015f\u0131la\u015ft\u0131rma say\u0131s\u0131n\u0131 azaltarak g\u00fc\u00e7 \u00f6d\u00fcnle\u015fimlerini y\u00f6netme<\/h3>\n\n\n\n<p>ANOVA sonras\u0131 \u00e7oklu kar\u015f\u0131la\u015ft\u0131rmalar\u0131n do\u011fas\u0131nda bulunan potansiyel tuzaklar\u0131 ele almak i\u00e7in, ara\u015ft\u0131rmac\u0131lar yeterli istatistiksel g\u00fcc\u00fc korumak ve \u015fi\u015firilmi\u015f tip I hata riskini (yanl\u0131\u015f pozitifler) kontrol etmek aras\u0131ndaki uzla\u015fmay\u0131 makul bir \u015fekilde y\u00f6netmelidir. \u0130\u015fte etkili stratejiler:<\/p>\n\n\n\n<ul>\n<li>\u00d6nceliklendirme: Hangi kar\u015f\u0131la\u015ft\u0131rmalar\u0131n hipotezleriniz i\u00e7in en hayati \u00f6neme sahip oldu\u011funu belirleyin ve daha fazla inceleme i\u00e7in bunlara \u00f6ncelik verin.<\/li>\n\n\n\n<li>Konsolidasyon: Tedavi seviyeleri aras\u0131ndaki t\u00fcm olas\u0131 ikili kar\u015f\u0131la\u015ft\u0131rmalar\u0131 incelemek yerine, yaln\u0131zca her bir tedavi grubunu kontrol ile kar\u015f\u0131la\u015ft\u0131rmaya odaklan\u0131n veya tedavi gruplar\u0131n\u0131 anlaml\u0131 kategoriler halinde birle\u015ftirin.<\/li>\n<\/ul>\n\n\n\n<p>Ara\u015ft\u0131rmac\u0131lar d\u00fc\u015f\u00fcnceli bir \u015fekilde daha az say\u0131da kar\u015f\u0131la\u015ft\u0131rma se\u00e7erek, sadece \u00e7al\u0131\u015fmalar\u0131n\u0131n sa\u011flam istatistiksel g\u00fcc\u00fcn\u00fc koruma \u015fans\u0131n\u0131 art\u0131rmakla kalmaz, ayn\u0131 zamanda ke\u015fif potansiyellerini t\u00fcketen ezici d\u00fczeltme prosed\u00fcrleri olmadan deneysel hata oran\u0131n\u0131 da azalt\u0131rlar.<\/p>\n\n\n\n<p>Bu hassas dengenin ak\u0131ll\u0131ca ele al\u0131nmas\u0131, metodolojik titizli\u011fi teyit ederken \u00f6nemli bulgular\u0131n \u00f6ne \u00e7\u0131kmas\u0131n\u0131 sa\u011flar - ANOVA \u00e7er\u00e7evesini takiben post hoc testi kullanan t\u00fcm \u00e7al\u0131\u015fmalar i\u00e7in temel bir denge noktas\u0131d\u0131r.<\/p>\n\n\n\n<h2 id=\"h-summary-and-conclusion\">\u00d6zet ve Sonu\u00e7<\/h2>\n\n\n\n<h3 id=\"h-recap-of-key-points-covered-in-the-content-outline\">\u0130\u00e7erik tasla\u011f\u0131nda ele al\u0131nan kilit noktalar\u0131n \u00f6zeti<\/h3>\n\n\n\n<p>Bu makale boyunca, Varyans Analizi (ANOVA) ve onun kritik tamamlay\u0131c\u0131s\u0131 olan <strong>post hoc test ANOVA<\/strong>. Ba\u015flang\u0131\u00e7 olarak, \u00fc\u00e7 veya daha fazla ba\u011f\u0131ms\u0131z grubun ortalamalar\u0131 aras\u0131nda istatistiksel olarak anlaml\u0131 farkl\u0131l\u0131klar olup olmad\u0131\u011f\u0131n\u0131 ay\u0131rt etmek i\u00e7in kullan\u0131lan ANOVA hakk\u0131nda temel bir anlay\u0131\u015f olu\u015fturduk.<\/p>\n\n\n\n<p>\u0130lk ANOVA anlaml\u0131 sonu\u00e7lar verdi\u011finde gerekli olan post hoc testinin inceliklerini ara\u015ft\u0131rd\u0131k. Bir ANOVA bize en az iki grubun farkl\u0131 oldu\u011funu s\u00f6ylerken, hangi gruplar\u0131n veya ka\u00e7 grubun birbirinden farkl\u0131 oldu\u011funu belirtmedi\u011fini tespit ettik. \u0130\u015fte burada post hoc testleri devreye girer.<\/p>\n\n\n\n<p>Tart\u0131\u015f\u0131rken yolculuk bizi \u00e7e\u015fitli d\u00f6neme\u00e7lerden ge\u00e7irdi:<\/p>\n\n\n\n<ul>\n<li>Genel varyans\u0131 belirlemek i\u00e7in F-istatisti\u011fini kullanan ANOVA'n\u0131n omnibus testinin kritik do\u011fas\u0131.<\/li>\n\n\n\n<li>Sa\u011fl\u0131kl\u0131 istatistiksel analiz i\u00e7in bu sonu\u00e7lar\u0131 do\u011fru yorumlaman\u0131n \u00f6nemi.<\/li>\n<\/ul>\n\n\n\n<p>Deneysel hata oranlar\u0131 gibi s\u0131n\u0131rlamalar kendini g\u00f6sterdi\u011finde, post hoc testlerin neden sadece yararl\u0131 de\u011fil ayn\u0131 zamanda gerekli oldu\u011funu anlad\u0131k. Bu hata oranlar\u0131n\u0131 kontrol ederek ve I. tip hata olas\u0131l\u0131\u011f\u0131n\u0131 \u015fi\u015firmeden \u00e7oklu kar\u015f\u0131la\u015ft\u0131rmalara izin vererek rafine i\u00e7g\u00f6r\u00fcler sunar.<\/p>\n\n\n\n<p>Tukey, Holm ve Dunnett gibi farkl\u0131 y\u00f6ntemleri incelerken, muhtemelen bu y\u00f6ntemlerin benzersiz ama\u00e7lara hizmet etti\u011fini fark etmi\u015fsinizdir - t\u00fcm olas\u0131 ortalama \u00e7iftlerinin \u00e7oklu kar\u015f\u0131la\u015ft\u0131rmalar\u0131n\u0131 yapmak veya tek bir kontrol grubu kar\u015f\u0131la\u015ft\u0131rmas\u0131na odaklanmak gibi.<\/p>\n\n\n\n<p>Post hoc test se\u00e7imi dikkatli bir de\u011ferlendirme gerektirir. Hata oran\u0131 kontrol\u00fc tek ba\u015f\u0131na ger\u00e7ekle\u015fmez; post hoc testleri se\u00e7erken, aile baz\u0131nda hata oranlar\u0131yla ilgili fakt\u00f6rleri tartmak gerekir.<\/p>\n\n\n\n<p>Tart\u0131\u015fmam\u0131za ger\u00e7ek d\u00fcnyadan \u00f6rnekler eklemek, bu kavramsal d\u00fc\u015f\u00fcncelerin pratik uygulama senaryolar\u0131 i\u00e7inde sa\u011flam bir \u015fekilde temellendirilmesine yard\u0131mc\u0131 oldu.<\/p>\n\n\n\n<p>Son olarak, ancak daha da \u00f6nemlisi, istatistiksel g\u00fcce de\u011findik. Kar\u015f\u0131la\u015ft\u0131rma say\u0131s\u0131n\u0131n azalt\u0131lmas\u0131 bazen g\u00fc\u00e7 \u00f6d\u00fcnle\u015fimlerinin azalt\u0131lmas\u0131 olarak g\u00f6r\u00fclse de, burada stratejik karar verme, burada \u00e7oklu post hoc testlerle u\u011fra\u015f\u0131rken bile bulgularda sa\u011flaml\u0131k sa\u011flar.<\/p>\n\n\n\n<h3 id=\"h-concluding-thoughts-on-the-importance-and-significance-of-post-hoc-testing-in-anova\">ANOVA'da post hoc testinin \u00f6nemi ve anlam\u0131 \u00fczerine son d\u00fc\u015f\u00fcnceler<\/h3>\n\n\n\n<p>Bu anlay\u0131\u015fl\u0131 geziyi sonu\u00e7land\u0131rmak i\u00e7in <strong>post hoc test ANOVA<\/strong>\u0130statistiksel analizin bu \u00f6zel alan\u0131na derinlemesine dalman\u0131n neden bu kadar \u00f6nemli oldu\u011funu kendimize hat\u0131rlatal\u0131m. Sa\u011fl\u0131k alan\u0131ndaki at\u0131l\u0131mlardan \u00e7\u0131\u011f\u0131r a\u00e7an teknolojik geli\u015fmelere kadar uzanan ara\u015ft\u0131rma ba\u011flamlar\u0131nda, bulgular\u0131m\u0131z\u0131n yaln\u0131zca istatistiksel olarak anlaml\u0131 de\u011fil, ayn\u0131 zamanda pratik olarak da anlaml\u0131 olmas\u0131n\u0131 sa\u011flamak b\u00fcy\u00fck fark yaratabilir.<\/p>\n\n\n\n<p>ANOVA'y\u0131 takiben post hoc testlerin ak\u0131ll\u0131ca kullan\u0131lmas\u0131, sadece farkl\u0131l\u0131klar\u0131 tespit etmenin \u00f6tesine ge\u00e7memize ve bu farkl\u0131l\u0131klar\u0131n ne oldu\u011funu ve boyutlar\u0131n\u0131, sonraki ara\u015ft\u0131rma yollar\u0131n\u0131 kararl\u0131 bir \u015fekilde veya politika kararlar\u0131n\u0131 etkili bir \u015fekilde etkileyecek kadar hassas ve g\u00fcvenle ke\u015ffetmeye giri\u015fmemize olanak tan\u0131r.<\/p>\n\n\n\n<p>Hevesli akademisyenler ve kendini i\u015fine adam\u0131\u015f profesyoneller olarak giderek daha fazla veri odakl\u0131 bir d\u00fcnyada gezinirken, bu gibi yakla\u015f\u0131mlar yaln\u0131zca anlay\u0131\u015f\u0131m\u0131z\u0131 geli\u015ftirmekle kalm\u0131yor, ayn\u0131 zamanda olas\u0131l\u0131klar\u0131 da geni\u015fletiyor. Post hoc testler, bazen bunalt\u0131c\u0131 veri k\u00fcmeleri aras\u0131nda n\u00fcansl\u0131 ayr\u0131nt\u0131lar\u0131 ayd\u0131nlatan bir me\u015faleyi y\u00fcksekte tutmaya devam ediyor - hem bilimsel \u00e7evrelerde hem de sahalarda incelemeye kar\u015f\u0131 hararetle duran sa\u011flam analitik s\u00fcre\u00e7lere dayal\u0131 bilin\u00e7li kararlar alma yetene\u011fimizi art\u0131ran kesin i\u00e7g\u00f6r\u00fclere do\u011fru yol g\u00f6steren bir fener, toplumsal faydalar i\u00e7in ciddiyetle takip edilen yeniliklere \u00f6nc\u00fcl\u00fck ediyor her yeni aray\u0131\u015fa ilham veren \u015feye sad\u0131k kalarak kapsam\u0131 \u00e7ok boyutlu '...\u00f6ng\u00f6r\u00fclemeyen modeller i\u00e7in'.<\/p>\n\n\n\n<p>Her \u015feye ra\u011fmen umudum sabit: kendi analizleriniz, \u00f6vg\u00fcy\u00fc hak eden netlikle serpi\u015ftirilmi\u015f verimli bir anlay\u0131\u015f sa\u011flas\u0131n ve nihayetinde kan\u0131ta dayal\u0131 uygulamalar\u0131n dokundu\u011fu ya\u015famlar\u0131 iyile\u015ftirsin... her zaman zor ama ebediyen cazip olan ger\u00e7e\u011fin pe\u015finde, yorulmadan s\u00fcren farkl\u0131l\u0131\u011f\u0131 tan\u0131mlayan titiz istatistiksel temeller \u00fczerinde zamans\u0131z bir vasiyet gibi dursun.<\/p>\n\n\n\n<h2 id=\"h-experience-the-power-of-visual-mastery-simplifying-complexity-with-mind-the-graph\"><br>G\u00f6rsel Ustal\u0131\u011f\u0131n G\u00fcc\u00fcn\u00fc Deneyimleyin: Mind the Graph ile Karma\u015f\u0131kl\u0131\u011f\u0131 Basitle\u015ftirin!<\/h2>\n\n\n\n<p>Karma\u015f\u0131k kavramlar\u0131 anlama \u015feklinizi yeniden tan\u0131mlarken kusursuz g\u00f6rsel ileti\u015fimin potansiyelini ortaya \u00e7\u0131kar\u0131n. G\u00f6rsellerin hakim oldu\u011fu bir \u00e7a\u011fda, kuantum fizi\u011fi gibi esrarengiz bir konuda bile karma\u015f\u0131k fikirleri anlamak, grafiklerin kat\u0131ks\u0131z etkinli\u011fi sayesinde \u00e7ocuk oyunca\u011f\u0131 haline geliyor.<\/p>\n\n\n\n<p>ile g\u00f6rsel yolculu\u011funuza \u00e7\u0131k\u0131n <a href=\"https:\/\/mindthegraph.com\/?utm_source=blog&amp;utm_medium=content\" target=\"_blank\" rel=\"noreferrer noopener\">Mind the Graph<\/a>Karma\u015f\u0131k mesajlar\u0131 b\u00fcy\u00fcleyici g\u00f6rsellere d\u00f6n\u00fc\u015ft\u00fcrmede en b\u00fcy\u00fck yard\u0131mc\u0131n\u0131z. Galerimizdeki titizlikle haz\u0131rlanm\u0131\u015f binden fazla ill\u00fcstrasyon ile olas\u0131l\u0131klar s\u0131n\u0131rs\u0131zd\u0131r. Son teknoloji \u00fcr\u00fcn\u00fc ak\u0131ll\u0131 poster arac\u0131m\u0131z, zahmetsizce \u00f6ne \u00e7\u0131kan posterler olu\u015fturman\u0131z\u0131 sa\u011flar.<\/p>\n\n\n\n<p>Size \u00f6zel bir g\u00f6rsel \u015fahesere sahip olmak varken neden s\u0131radan olanla yetinesiniz? \u0130ll\u00fcstrasyonlar\u0131 benzersiz ihtiya\u00e7lar\u0131n\u0131za g\u00f6re \u00f6zelle\u015ftirmek i\u00e7in yetenekli ekibimizin uzmanl\u0131\u011f\u0131ndan yararlan\u0131n. Mind the Graph sadece bir ara\u00e7 de\u011fildir; g\u00f6rsellerin kelimelerden daha y\u00fcksek sesle konu\u015ftu\u011fu bir d\u00fcnyaya a\u00e7\u0131lan kap\u0131n\u0131zd\u0131r.<\/p>\n\n\n\n<p>\u0130leti\u015fim oyununuzu g\u00fc\u00e7lendirmeye haz\u0131r m\u0131s\u0131n\u0131z? \u00dccretsiz kaydolun ve hemen olu\u015fturmaya ba\u015flay\u0131n. Sizin mesaj\u0131n\u0131z, bizim g\u00f6rsellerimiz - kusursuz bir kombinasyon!<\/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\"><a href=\"https:\/\/mindthegraph.com\/?utm_source=blog&amp;utm_medium=content\"><img decoding=\"async\" loading=\"lazy\" width=\"648\" height=\"535\" src=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2022\/11\/beautiful-poster-templates.png\" alt=\"g\u00fczel-poster-\u015fablonlar\u0131\" class=\"wp-image-25482\" srcset=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2022\/11\/beautiful-poster-templates.png 648w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2022\/11\/beautiful-poster-templates-300x248.png 300w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2022\/11\/beautiful-poster-templates-15x12.png 15w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2022\/11\/beautiful-poster-templates-100x83.png 100w\" sizes=\"(max-width: 648px) 100vw, 648px\" \/><\/a><\/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\/?utm_source=blog&amp;utm_medium=content\" style=\"border-radius:50px;background-color:#dc1866\" target=\"_blank\" rel=\"noreferrer noopener\">Mind the Graph ile yaratmaya ba\u015flay\u0131n<\/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>Post hoc test ANOVA'n\u0131n t\u00fcm inceliklerini ke\u015ffedin. \u0130statistiksel analizinizi m\u00fckemmelle\u015ftirin ve veri setlerinizin \u00f6nemini ortaya \u00e7\u0131kar\u0131n.<\/p>","protected":false},"author":4,"featured_media":50304,"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 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Post Hoc Testing ANOVA: Learn How to Analyze Data Sets<\/title>\n<meta name=\"description\" content=\"Discover the ins and outs of post hoc testing ANOVA. 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Perfect your statistical analysis and uncover the significance of your data sets.","og_url":"https:\/\/mindthegraph.com\/blog\/tr\/anova-sonrasi-test\/","og_site_name":"Mind the Graph Blog","article_published_time":"2024-02-11T14:03:02+00:00","article_modified_time":"2024-02-07T14:16:52+00:00","og_image":[{"width":1124,"height":613,"url":"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/02\/post-hoc-testing-anova-blog.jpg","type":"image\/jpeg"}],"author":"Fabricio Pamplona","twitter_card":"summary_large_image","twitter_title":"Post Hoc Testing ANOVA: Learn How to Analyze Data Sets","twitter_description":"Discover the ins and outs of post hoc testing ANOVA. Perfect your statistical analysis and uncover the significance of your data sets.","twitter_image":"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/02\/post-hoc-testing-anova-blog.jpg","twitter_misc":{"Written by":"Fabricio Pamplona","Est. reading time":"18 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/mindthegraph.com\/blog\/post-hoc-testing-anova\/","url":"https:\/\/mindthegraph.com\/blog\/post-hoc-testing-anova\/","name":"Post Hoc Testing ANOVA: Learn How to Analyze Data Sets","isPartOf":{"@id":"https:\/\/mindthegraph.com\/blog\/#website"},"datePublished":"2024-02-11T14:03:02+00:00","dateModified":"2024-02-07T14:16:52+00:00","author":{"@id":"https:\/\/mindthegraph.com\/blog\/#\/schema\/person\/c8eaee6d8007ac319523c3ddc98cedd3"},"description":"Discover the ins and outs of post hoc testing ANOVA. 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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. Connect with him on LinkedIn - Fabricio Pamplona.","sameAs":["http:\/\/mindthegraph.com","https:\/\/www.linkedin.com\/in\/fabriciopamplona"],"url":"https:\/\/mindthegraph.com\/blog\/tr\/author\/fabricio\/"}]}},"_links":{"self":[{"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/posts\/50301"}],"collection":[{"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/comments?post=50301"}],"version-history":[{"count":3,"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/posts\/50301\/revisions"}],"predecessor-version":[{"id":50305,"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/posts\/50301\/revisions\/50305"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/media\/50304"}],"wp:attachment":[{"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/media?parent=50301"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/categories?post=50301"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mindthegraph.com\/blog\/tr\/wp-json\/wp\/v2\/tags?post=50301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}