Name: using the q test to flag outlier data
Uploaded: 2024-04-04T10:51:12.000Z
Duration: 1 min
Channel: JoVE

K12 Standards

Texas Essential Knowledge and Skills

High School

Chemistry

CHEM.2

CHEM.2.C

Detection of Gross Error: The Q Test

Wartości odstające to te punkty danych, które bardzo różnią się od reszty zestawu danych.
Test Q Dixona to test istotności, który pomaga określić, czy zachować lub odrzucić te niespójne punkty danych w zestawie danych.
Matematycznie statystyka testu Q jest stosunkiem bezwzględnej różnicy między wartością odstającą a najbliższym punktem danych a zakresem populacji.
Statystyka testu Q lub iloraz odrzucenia jest następnie porównywany z tabelarycznie umieszczoną krytyczną wartością Q dla określonego poziomu istotności i stopni swobody.
Gdy iloraz odrzucenia jest równy lub większy niż wartość podana w tabeli, hipoteza zerowa jest odrzucana, a podejrzewana wartość odstająca może zostać odrzucona.
Wręcz przeciwnie, hipoteza zerowa jest akceptowana, gdy wartość doświadczalna jest mniejsza niż wartość Q przedstawiona w tabeli. Następnie podejrzana wartość odstająca musi zostać zachowana w zestawie danych.

wykrywanie dużego błędu: test q

Jeżeli jeden lub więcej punktów danych jest odległych od pozostałych danych, należy określić, czy są to wartości odstające i czy należy je wyeliminować ze zbioru danych, aby zapewnić dokładne odwzorowanie zmierzonej wartości. W wielu przypadkach wartości odstające wynikają z rażących błędów (lub błędów ludzkich) i nie odzwierciedlają dokładnie zjawiska leżącego u ich podstaw. Jednakże w niektórych przypadkach te pozorne wartości odstające odzwierciedlają prawdziwe różnice fenomenologiczne. W takich przypadkach możemy zastosować metody statystyczne, aby określić, czy zachować wartość odstającą. Metoda statystyczna, która może pomóc nam zachować lub odrzucić wartości odstające, nazywa się testem Q. Aby wykonać test Q, najpierw porządkujemy wartości w zbiorze danych w kolejności rosnącej wartości. Następnie obliczamy wartość Q, biorąc stosunek bezwzględnej różnicy między punktem danych a sąsiadującym z nim punktem danych. Tę wartość Q porównuje się następnie z tabelaryczną krytyczną wartością Q na wybranym poziomie istotności i odpowiednich stopniach swobody. Jeśli wartość Q jest równa lub większa od wartości referencyjnej Q w tabeli, punkt danych jest uważany za wartość odstającą i dlatego jest odrzucany ze zbioru danych. W tym przypadku rozsądne jest pominięcie punktu danych jako wartości odstającej, ponieważ wielkości odchylenia nie można logicznie wytłumaczyć błędami losowymi (nieokreślonymi). Z drugiej strony, jeśli wartość Q jest mniejsza niż wartość odniesienia w tabeli, punkt danych należy zachować, a interpretacja jest taka, że ​​różnica między tym punktem danych a resztą danych mieści się w rozsądnych (statystycznych) oczekiwaniach.

Wykrywanie dużego błędu: Test Q

Wartości odstające to te punkty danych, które bardzo różnią się od reszty zestawu danych.
Test Q Dixona to test istotności, który pomaga określić, czy ...

Education

JoVE Core

Analytical Chemistry

Chemia analityczna

Unit 1

Jednostka 1

Chemical Applications of Statistical Analyses

Zastosowania analiz statystycznych w chemii

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

KLUCZOWE TERMINY I POJĘCIA

using the q test to flag outlier data

The Q test helps identify outlier data points that sit far from the rest of a data set. In high school lab work, this can help students decide whether an unusual value should stay in the data or be removed. Outliers often come from gross errors, or human mistakes, and may not reflect the real result being measured.

Some points that look like outliers may still reflect a true difference in the phenomenon being studied. In those cases, a statistical test can help judge whether the value should be kept or rejected. The Q test provides that check by comparing a suspect point with the rest of the ordered data.

To perform the Q test, the values are first arranged in increasing order. Next, the Q value is calculated as the ratio of the absolute difference between a data point and its adjacent data point. That Q value is then compared with the tabulated critical Q value for a chosen significance level and the correct degrees of freedom.

If the calculated Q value is equal to or greater than the table value, the point is treated as an outlier and rejected from the data set. In that case, the deviation is considered too large to be explained by random, indeterminate errors. If the Q value is smaller than the reference value, the point is retained because the difference is within normal statistical expectation.

Using the Q Test to Flag Outlier Data

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detection of gross error: the q test

The Q test helps identify outlier data points that sit far from the rest of a data set. In high school lab work, this can help students decide whether an unusual value should stay in the data or be removed. Outliers often come from gross errors, or human mistakes, and may not reflect the real result being measured. Some points that look like outliers may still reflect a true difference in the phenomenon being studied. In those cases, a statistical test can help judge whether the value should...

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key terms and concepts

Learn how the Q test helps students identify outlier data, compare a suspected value to a critical table value, and decide whether to keep or reject it.

Using the Q Test to Flag Outlier Data | JoVE K12

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Using the Q Test to Flag Outlier Data

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