Kusanthura-Maŵiro
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Kusanthura-Maŵiro (kufuma ku ChiGerman: Statistik, orig. "kulongosora boma, la charu")[1][2] ni kafukufuku uyo wakukhwaskana na kuwunjika, kwendeska, kusanda, kung'anamura, na kulongosora uthenga.[3][4][5] Para tikugwiliskira ntchito Kusanthura-Maŵiro pa suzgo la sayansi, malonda, panji la umoyo wa ŵanthu, kanandi tikwamba na Unandi-wose panjiso kayezgero ka Kusanthura-Maŵiro ako katiŵazgikenge. Unandi-wose, ungaŵa vigaŵa vyakupambana pambana vya ŵanthu panji vinthu nga "ŵanthu ŵose ŵakukhala mu charu" panji "chimenyu chilichose chakuwumba khristoli". Kusanthura-Maŵiro kukuwona vyose vyakukhwaskana na umo vinthu viliri, kusazgapo ndondomeko ya kunozga ma Save na ma kafukufuku.[6]
Pala uthenga wa chipendero ungatoleleka yayi, waKusanthura-Maŵiro wakutolela uthenga pakupanga makafukufuku na ma save ago ghakugwiliska ntchito viyezgero. Kutola viyezgero vyakwimilra yikuwoneseska kuti fundo zingatandazga mwa mahara kufuma ku kachiyezgero kuluta ku unandi-wose mwanthunthu. Kafukufuku wakusanda kukukhwaska kupima vinthu ivyo vikusangika mu ndondomeko iyo yikusambizgika, kunjizgapo vinthu vinyake ivyo vikusintha vinthu ivi, na kupima vinthu vinyake ivyo vikuchitika mwakuyana na ndondomeko iyo yikapangika kuti ŵanthu ŵamanye usange vinthu ivi vyasintha vinthu ivyo vikupima. Mwakupambaniska, kusanda Kwakulaŵilira kukukhwaska kafukufuku wakuseŵeleska yayi.
Vyakudanga viŵiri mwa nthowa za Kusanthura-Maŵiro yakugwiliskika mu kusanda uthenga: Kusanthura-Maŵiro kwakulongosoleka, Ivyo vikuyowoya mwakudumura ivyo vyafuma mu viyezgero kugwiriska maindekse nthe tikatikati panji yisezgo yakukhazikika, na Kusanthura-Maŵiro mwa fundo, izo zikufunya fundo kufuma pa uthenga uwo ukupambanangapambananga (nthe, vyakubekeka mwautesi, upambano wa viyezgero).[7] Kusanthura-Maŵiro ghakulongosoleka kanandi ghakukhwaska magulu yaŵiri ya maunenesko gha chigaŵo (chiyezgero panji unandi-wose): uzgoloŵezgi wa pakati (pangi malo) uikukhumba kulongora umo vinthu viliri pakatikati panji umo vikuŵira, apo kusakatuka (panji kuti kusinthasintha) kukulongora umo ŵanthu ŵakufumira pakati pa vinthu. Fundo zakuroska mu Kusanthura-Maŵiro musamuzi zikupangika pa chizengero cha fundo ya mayero, iyi yikulongosora umo vinthu vikuchitikira mwamwaŵi.
Kanandi ndondomeko ya Kusanthura-Maŵiro kukukhwaska kutolela kwa uthenga kulazgiska ku kuyezga kwa ubali pakati pa vibwayila viŵiri vya Kusanthura-Maŵiro, panji vibwayila vyauthenga na mauthenga ghakuwumbika na ŵanthu kufuma ku chiyezgero chakuwumbika mwausuma. Chiloskero chikupelekeka kulongosola ubali wa Kusanthura-Maŵiro pakati pa vibwayila viŵiri vyauthenga, ndipo this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual relationship between populations is missed giving a "false negative").
Statistical measurement processes are also prone to error in regards to the data that they generate. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also occur. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems.