Name: significance testing in analytical chemistry
Uploaded: 2024-04-04T10:51:12.000Z
Duration: 1 min 4 s
Channel: JoVE

K12 Standards

Texas Essential Knowledge and Skills

High School

Chemistry

CHEM.2

CHEM.2.C

Significance Testing: Overview

هل الفرق بين القيمتين يرجع إلى خطأ عشوائي غير قابل للتفسير أو خطأ منهجي يمكن ترشيده بواسطة نموذج افتراضي؟
اختبار الدلالة هو تحليل إحصائي يستخدم للتحقق مما إذا كان يمكن تفسير الفرق بين قيمتين من خلال أخطاء غير محددة أم لا.
تفترض الفرضية الصفرية أن القيم التي تمت مقارنتها هي نفسها وأن أي اختلاف ينبع من أخطاء غير محددة. تنص الفرضية البديلة على أن الاختلاف يجب أن يكون حقيقيا ولا يمكن تفسيره بأخطاء غير محددة.
يحدد مستوى الأهمية المشار إليه ب α شرط مستوى الثقة لصحة الفرضية الصفرية. يتم رفض الفرضية الصفرية عندما تكون القيم موجودة خارج مستوى الثقة.
يسمى اختبار الأهمية أحادي الذيل إذا حدث رفض للقيم في طرف واحد فقط من منحنى التوزيع الطبيعي. في اختبارات الدلالة ثنائية الذيل ، يمكن أن يحدث الرفض للقيم التي تقع في أي من طرفي منحنى التوزيع الطبيعي.

اختبار الأهمية: نظرة عامة

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اختبار الأهمية هو مجموعة من الأساليب الإحصائية المستخدمة لاختبار ما إذا كان الادعاء بشأن المعلمة صحيحًا. في الكيمياء التحليلية، يتم استخدام اختبار الأهمية في المقام الأول لتحديد ما إذا كان الفرق بين قيمتين يأتي من أخطاء محددة أو عشوائية. يمكن أن يؤدي تأثير تغيير معين في بروتوكول القياس أو المحلل أو العينة نفسها إلى انحراف عن النتيجة المتوقعة. في حالة وجود انحراف/قيمة متطرفة مشتبه بها، نحتاج إلى أن نكون قادرين على التأكيد رياضيًا أن الانحراف يأتي من مصدر محدد وأن الملاحظة مع الانحراف يمكن حذفها منطقيًا من التحليل.

يتم استخدام فرضيتين كمعايير لاختبار الأهمية. تنص الفرضية الصفرية (H0) على أن القيم التي تتم مقارنتها لا تختلف عن بعضها البعض بشكل كبير. وبعبارة أخرى، إذا وجد أي اختلاف بين قيمتين، فإنه يعزى إلى خطأ غير محدد. تنص الفرضية البديلة (HA) على أن القيم المقارنة غير متساوية، وأن الفرق أكبر مما يمكن تفسيره بخطأ غير محدد.

قبل إجراء الاختبار، يجب ذكر الفرضيات، ويجب تحديد مستوى الأهمية (α). يتم بعد ذلك حساب إحصائية الاختبار، استنادًا إلى متوسط ​​العينة والانحراف المعياري، ومقارنتها بالقيم المجدولة، والتي يتم تعيينها عند مستويات أهمية معينة ويتم تعريفها على أنها ذات ذيل واحد أو طرفين. إذا تجاوزت إحصائية الاختبار المحسوبة القيم الحرجة (الإحصاء المجدول)، يتم رفض الفرضية الصفرية، ونذكر أن الفرق بين القيمتين لا يمكن تفسيره بخطأ عشوائي غير محدد.

في اختبار الأهمية أحادي الطرف، يمكن للفرضية البديلة أن تحدد أن القيمة المرصودة إما أعلى أو أقل من القيمة المتوقعة، ولكن ليس كليهما. في اختبار الأهمية ثنائي الذيل، يمكن للفرضية البديلة أن تنص ببساطة على أن القيمة المرصودة لا تساوي القيمة المتوقعة، دون النظر إلى الاتجاه.

يمكن استخدام اختبار الأهمية على معلمات إحصائية مختلفة لواحدة أو أكثر من مجموعات البيانات. تُعطى الاختبارات أسماء مختلفة اعتمادًا على المعلمات أو الغرض. يتم تطبيق اختبار الأهمية بشكل متكرر لمقارنة قيمة ملحوظة مع المتوسط ​​أو مقارنة وسيلتين من مجموعتين مختلفتين من البيانات. تُعرف هذه الاختبارات باسم اختبارات t. يمكن أيضًا إجراء اختبارات الأهمية على تباين مجموعتين من البيانات. وفي هذه الحالة، يُعرف الاختبار باسم اختبار F. إذا تم استخدام اختبار الأهمية لتحديد القيم المتطرفة، فإن الاختبار يسمى اختبار Q.
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هل الفرق بين القيمتين يرجع إلى خطأ عشوائي غير قابل للتفسير أو خطأ منهجي يمكن ترشيده بواسطة نموذج افتراضي؟
ال...

Education

JoVE Core

Analytical Chemistry

الكيمياء التحليلية

Unit 1

الوحدة الأولى

Chemical Applications of Statistical Analyses

التطبيقات الكيميائية للتحليلات الإحصائية

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

المصطلحات والمفاهيم الأساسية

significance testing in analytical chemistry

Significance testing helps analytical chemists decide whether a difference is meaningful or just caused by random error. It is used to test whether a claim about a parameter is valid. In analytical chemistry, it mainly helps determine whether a change between two values comes from determinate error or indeterminate error.

A determinate error has a clear source. It may come from a change in the measurement protocol, the analyst, or the sample itself. These changes can create a deviation from the expected result. If a value looks like an outlier, significance testing can help show mathematically that the deviation comes from a determinate source and that the value can be logically left out of the analysis.

Significance testing begins with two hypotheses. The null hypothesis, H0, says the values being compared do not differ significantly. If any difference exists, it is treated as an indeterminate error. The alternate hypothesis, HA, says the values are not equal and that the difference is too large to be explained by indeterminate error.

Before the test is performed, the hypotheses must be stated and a significance level, α, must be set. The test statistic is then calculated from the sample mean and standard deviation. This value is compared with tabulated critical values, which are defined at specific significance levels and can be one-tailed or two-tailed. If the calculated test statistic is greater than the critical value, the null hypothesis is rejected.

A rejected null hypothesis means the difference cannot be explained by random, indeterminate error. In one-tailed significance testing, the alternative hypothesis says the observed value is either higher or lower than expected, but not both. In two-tailed testing, the alternative hypothesis only says the observed value is not equal to the expected value, without naming a direction.

Significance testing can be used on different statistical parameters from one or more data sets. When it compares an observed value with a mean or compares two means, it is called a t-test. When it compares the variance of two data sets, it is called an F-test. When it is used to identify outliers, it is called a Q-test.

Significance Testing in Analytical Chemistry

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significance testing: overview

Significance testing helps analytical chemists decide whether a difference is meaningful or just caused by random error. It is used to test whether a claim about a parameter is valid. In analytical chemistry, it mainly helps determine whether a change between two values comes from determinate error or indeterminate error. A determinate error has a clear source. It may come from a change in the measurement protocol, the analyst, or the sample itself. These changes can create a deviation from...

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key terms and concepts

Learn how significance testing helps identify random versus determinate error, set hypotheses, and choose t-tests, F-tests, or Q-tests in chemistry.

Significance Testing in Analytical Chemistry | JoVE K12

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Significance Testing in Analytical Chemistry

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