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

外れ値とは、他のデータ セットとは非常に異なるデータ ポイントです。
DixonのQ検定は、データセット内のこれらの一貫性のないデータポイントを保持するか拒否するかを決定するのに役立つ有意性検定です。
数学的には、Q検定統計量は、外れ値とそれに最も近いデータポイントとの間の絶対差と母集団の範囲との間の比です。
次に、Q 検定統計量または棄却指数が、特定の有意水準と自由度について表形式の臨界 Q 値と比較されます。
棄却指数が表形式の値以上の場合、帰無仮説は棄却され、疑わしい外れ値を棄却できます。
それどころか、帰無仮説は、実験値が表形式のQ値よりも小さい場合に受け入れられます。次に、疑わしい外れ値をデータ セットに保持する必要があります。

重大なエラーの検出: q テスト

1 つ以上のデータ ポイントが残りのデータから遠く離れている場合、それらが外れ値であるかどうか、また測定値を正確に表現するためにデータ セットから削除する必要があるかどうかを判断する必要があります。多くの場合、外れ値は重大なエラー (または人為的エラー) から発生し、根底にある現象を正確に反映していません。ただし、場合によっては、これらの明らかな外れ値が真の現象学的差異を反映していることがあります。このような場合、統計的手法を使用して、外れ値を保持するかどうかを決定できます。外れ値を保持または拒否するのに役立つ統計的手法は、Q テストと呼ばれます。 Q テストを実行するには、まずデータ セット内の値を値の増加順に並べます。次に、データ ポイントとその隣接するデータ ポイント間の絶対差の比率を取得して Q 値を計算します。次に、この Q 値は、選択された有意水準と適切な自由度で表にまとめられた臨界 Q 値と比較されます。 Q 値がテーブル内の基準 Q 値以上の場合、データ ポイントは外れ値とみなされ、データ セットから除外されます。ここで、偏差の大きさはランダムな (不定の) 誤差によって論理的に説明できないため、データ ポイントを外れ値として無視するのが合理的です。一方、Q 値が表の基準値より小さい場合、データ ポイントは保持される必要があり、このデータ ポイントと残りのデータの差は合理的な (統計的) 予想の範囲内であると解釈されます。

重大なエラーの検出: Q テスト

外れ値とは、他のデータ セットとは非常に異なるデータ ポイントです。
ディクソンのQ検定は、...

Education

JoVE Core

Analytical Chemistry

分析化学

Unit 1

ユニット1

Chemical Applications of Statistical Analyses

統計分析の化学的応用

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

主要用語と概念

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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systematic error: methodological and sampling errors

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Comparing Variance with the F-Test

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comparing experimental results: student's t-test

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Student's t-Test for Significance Checks

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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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Grubbs Test for Spotting Data Outliers

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ANOVA for Comparing Sample Means

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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

JoVE Core 分析化学 は、複雑な化学原理をアニメーションで視覚化することで、SI 単位から NMR 分光法を含むトピックをよりわかりやすくします。実験を撮影した実写動画では、これらの概念が世界中の研究所でどのように応用されているかを取り上げています。

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Using the Q Test to Flag Outlier Data

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