Name: atomic mass uncertainty in isotope samples
Uploaded: 2024-04-04T10:51:11.000Z
Duration: 1 min 10 s
Channel: JoVE

K12 Standards

Texas Essential Knowledge and Skills

High School

Chemistry

CHEM.2

CHEM.2.B

propagation-of-uncertainty-from-systematic-error

Propagation of Uncertainty from Systematic Error

異なる供給源から得られた元素の原子質量は、ある供給源から別の供給源への相対同位体濃度の変動によりわずかに変化します。これは、系統誤差から生じる不確実性の一例です。
ここで、原子質量の分布は、ランダムエラーで観察されるガウス分布とは異なり、長方形です。これは、最小値と最大値の間の値を見つける確率がほぼ等しいことを意味します。
プロットから計算される標準偏差または標準不確実性は、不確実性の尺度として機能します。
分子を構成するさまざまな原子からの個々の不確かさは、分子量の正味の不確かさに寄与します。
ヘテロ原子分子の分子量における系統的不確かさの伝播は、同一原子の数にそれらの標準不確かさを掛け、続いて分子内に存在するすべての元素からの総標準不確かさの二乗を合計し、最後に平方根を求めることによって推定されます。

系統的誤差による不確実性の伝播

元素の原子量は、その同位体の相対比によって変化します。一方、サンプルの酸素同位体の相対比率は、その平均原子量に影響します。たとえば、サンプルから酸素の原子質量を測定する場合、その質量はサンプル内の酸素の同位体質量の加重平均になります。単一のサンプルが地球上のすべての酸素分子の酸素の真の原子量を完全に反映している可能性は低いため、この特定のサンプルから得られる質量にはサンプリング誤差または一種の系統誤差が生じます。これは、酸素の原子量の不確実性です。これはランダムなエラーではなく、システムに固有のエラーまたは系統的なエラーです。特定の値が見つかる確率は、不確実性の範囲内では同じです。値の分布プロットは長方形の形状をしています。報告された最小値と最大値の出現頻度が大幅に減少しているため、その間の値の出現頻度はほぼ同じになります。これは、ランダムな誤差を伴う値のガウス分布とは異なります。ガウス分布では、発生頻度は中心値または平均値付近で高く、値が平均から外側に離れるにつれて減少します。

長方形の分布プロットから得られる標準偏差または標準不確実性を使用して、不確実性を測定できます。分子内の構成原子からの個々の不確実性は、その分子量の不確実性に伝播します。系統的誤差の伝播は、ランダムな誤差の伝播と同じ規則に従いません。系統的誤差は測定を繰り返しても減少しないため、その伝播はさらに相加的になります。次の例を考えてみましょう。複数の同一の原子を含む分子では、1 つの原子の質量の不確実性に類似の原子の数が乗算され、それらの特定の原子の分子量に対する不確実性の合計の寄与が求められます。分子内にヘテロ原子が存在する場合、分子質量の不確実性は、存在する各種類の原子からの不確実性の合計の 2 乗の合計の平方根になります。

異なる供給源から得られた元素の原子質量は、1つの供給源から...

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

atomic mass uncertainty in isotope samples

Atomic mass can vary because different samples contain different isotope ratios. For oxygen, the measured atomic mass is a weighted average of the isotopic masses in that sample. A single sample may not match the true atomic mass of oxygen found across all oxygen molecules on Earth. That creates sampling error, which is a type of systematic error.

This kind of uncertainty is not random. It is tied to the system being measured. Within the uncertainty range, the chance of finding a particular value is the same. The values form a rectangular distribution, with a sharp cutoff at the minimum and maximum reported values. The values in between appear at roughly the same frequency.

This pattern is different from a Gaussian distribution, which is typical of random error. In a Gaussian plot, values cluster near the mean and taper off as they move away from it. The rectangular distribution can be used to find the standard deviation, also called the standard uncertainty. That value describes the uncertainty in the atomic mass measurement.

The uncertainty in individual atoms also affects the uncertainty in a molecule’s mass. Systematic errors do not decrease with repeated measurements, so their propagation is more additive. For a molecule with several identical atoms, the uncertainty for one atom is multiplied by the number of those atoms. If a molecule contains different kinds of atoms, the total molecular mass uncertainty is found by taking the square root of the sum of the squares of the total uncertainties from each atom type.

Atomic Mass Uncertainty in Isotope Samples

atomic-mass-uncertainty-in-isotope-samples

si units: 2019 redefinition

SI Units in Chemistry Measurements

SI units give chemistry measurements a common standard. A measurement has two parts: a number that shows magnitude and a unit that gives the comparison scale. Using the same unit system helps keep results consistent and reduces mistakes in scientific communication. The Système International d'Unités, or SI units, includes both fundamental units and derived SI units. Fundamental units describe base physical quantities such as mass, distance, temperature, time, electric current, luminous...

degrees of freedom

Degrees of Freedom in Statistical Calculations

Degrees of freedom describe how many values in a statistical calculation are free to vary. They represent the minimum number of independent numbers needed to determine a statistic. In a data set, some values may be independent while others depend on them. A simple example uses three unknown numbers with a mean of 10. The first two numbers can be chosen freely. The last number cannot be chosen on its own because it must keep the mean at 10. In that case, the data set has two degrees of freedom.<br...

statistical analysis: overview

Mean, Median, Range, Precision & Accuracy

Statistical analysis helps compare repeated measurements and judge how good the results are. When the same sample is measured again and again, small differences can appear. These differences are called errors. Researchers use statistics to describe the data and to decide whether a method is suitable. The mean and median are common ways to find the central value of a data set. The mean is the sum of all results divided by the total number of results. The median is the middle value after the...

types of errors: detection and minimization

types-of-errors-detection-and-minimization

Measurement Errors: Sources and Examples

Measurement errors are the differences between an obtained result and the true value or estimated central value. They can be written as absolute error or relative error. Absolute error is the numerical difference from the true or central value. Relative error compares that difference to the true or central value and is often shown as a percentage. Errors can also be grouped by their source, magnitude, and sign. In many experiments, the main categories are systematic, random, and gross errors.

systematic error: methodological and sampling errors

systematic-error-methodological-and-sampling-errors

Reducing Bias in Measurement Errors

Systematic errors are bias in measurement, and their sources can often be identified and reduced. In this topic, the main categories are sampling, instrumental, methodological, and personal errors. Each one comes from a different part of the experiment, so each one needs a different fix. Sampling errors happen when the sampling method is poor or when the wrong population is chosen. These errors can be reduced by improving the sampling strategy. Better sample selection helps the results...

random error

Random Error in Measurements

Random error in measurements comes from uncontrollable variables that affect data from one trial to the next. These variables can include changes in the environment, imperfections in an instrument, or the natural variation of the thing being measured. Because the direction and size of the error can change from one measurement to the next, random error is hard to predict, estimate, or describe directly. Although random error is difficult to remove, its overall effect can be studied in a large...

standard deviation of calculated results

Reading Data Spread with Standard Deviation

Standard deviation describes how spread out data are around the mean. It helps show how tightly values cluster near the center and how far they sit from that central value. Many large data sets follow a Gaussian distribution, also called a normal distribution. A Gaussian distribution has a bell-shaped curve. The mean, or central value, appears in the middle, where the most frequent values are found. As data points move farther from the center, deviation increases and frequency decreases. The...

introduction to z scores

Reading z Scores for Outliers

z scores show how far a value is from the mean in standard deviation units. A z score, also called a standardized value, tells whether a data point sits above or below the mean, μ. Values larger than the mean have positive z scores, while values smaller than the mean have negative z scores. If x equals the mean, then the z score is zero. The z score scale is centered on zero. That means the mean of all z scores is zero, and their standard deviation is one. This makes z scores useful for...

uncertainty: overview

Measuring and Reporting Uncertainty

Uncertainty is part of analytical chemistry measurement. When repeated measurements are taken, random errors can cause the results to differ in small ways. These repeated values are compared with the estimated or expected value to judge how much uncertainty is present. Uncertainty is usually written after the estimated or expected value. It should also be reported with the correct number of significant figures, which are the digits needed to show a precise result. The possible variation from...

propagation of uncertainty from random error

propagation-of-uncertainty-from-random-error

How Uncertainty Changes in Calculations

Uncertainty changes as measurements move through calculations. In a multi-step experiment, each measurement adds some uncertainty. Because the steps happen one after another, the uncertainty from one step carries into the next step and affects the final result. The way uncertainty is combined depends on the math operation. For addition and subtraction, the final result is reported with absolute uncertainty. Absolute uncertainty is found as the square root of the sum of the absolute...

propagation of uncertainty from systematic error

Atomic mass can vary because different samples contain different isotope ratios. For oxygen, the measured atomic mass is a weighted average of the isotopic masses in that sample. A single sample may not match the true atomic mass of oxygen found across all oxygen molecules on Earth. That creates sampling error, which is a type of systematic error. This kind of uncertainty is not random. It is tied to the system being measured. Within the uncertainty range, the chance of finding a particular...

uncertainty: confidence intervals

Confidence Intervals and Sample Size

Confidence intervals show a range of values around a sample mean that may contain the true mean. They are written as a probability percentage. A 95% confidence interval means the statistician is 95% confident that the true mean lies within that range. The two ends of the range are called confidence limits. These limits are estimated from the sample mean, the standard deviation, and the statistical factor t, or t-score. The t-score depends on the number of degrees of freedom and the confidence...

significance testing: overview

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...

identifying statistically significant differences: the f-test

identifying-statistically-significant-differences-the-f-test

Comparing Variance with the F-Test

The F-test compares variance in two data sets or compares a sample variance with a population variance. It helps decide whether an indeterminate error can explain a difference in the values. In statistics, variance shows how spread out the data are. The test assumes that the data set or sets are normally distributed. It also assumes that the data sets are independent of each other. These conditions must be met before the F-test is used. The F test statistic is found by dividing one variance...

comparing experimental results: student's t-test

comparing-experimental-results-student-s-t-test

Student's t-Test for Significance Checks

Student's t-test is a statistical tool for checking whether two means are different in a meaningful way. It can compare a sample mean with a population mean, or it can compare the means from two data sets. The test uses the mean, standard deviation, and number of measurements, along with a chosen confidence interval. The test statistic is then compared with a table of critical values at that confidence level. If the test statistic is smaller than the critical value, the null hypothesis is...

detection of gross error: the q test

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...

calibration curves: linear least squares

Using a Calibration Line to Find Unknowns

A calibration curve links an instrument's signal to known concentrations of a substance. Scientists use it to set response levels with standards. They can also fit an equation to the curve and use it to find the concentration of unknown samples. When the data make a straight line, the linear least-squares method is the standard way to fit the curve. This method finds the line that best matches the points by minimizing the sum of the squared differences between the predicted and actual values.

calibration curves: correlation coefficient

calibration-curves-correlation-coefficient

Interpreting Correlation in Calibration Curves

Calibration curves use the correlation coefficient, written as r, to show how strongly two variables are related and which direction the relationship takes. In a linear calibration curve, this value helps describe the association between the variables being compared. The correlation coefficient ranges from -1 to +1. A value of +1 means a perfect positive linear correlation, while -1 means a perfect negative correlation. A value of 0 means there is no correlation between the two variables. A...

correlation and regression

Using Regression to Measure Variable Links

Correlation and regression are used to study the relationship between two variables. Correlation shows how strongly the variables are associated. Regression helps describe that relationship with a line that can be used for prediction. In linear regression, the relationship is written as a correlation coefficient. This coefficient is shown as r and ranges from -1 to +1. A positive value means the variables move in the same direction. A negative value means they move in opposite directions. <br...

difference from background: limit of detection

difference-from-background-limit-of-detection

Limit of Detection and Quantification

The limit of detection, or LOD, is the smallest amount of analyte that can be distinguished from background noise. In measurement science, background noise is the natural signal in a sample that can interfere with the analyte signal. The LOD helps show whether a substance is present, but it does not usually give a reliable amount. The LOD is set at the concentration where the analyte signal is three times larger than the standard deviation of the blank signal. A blank is a sample without the...

quantifying and rejecting outliers: the grubbs test

quantifying-and-rejecting-outliers-the-grubbs-test

Grubbs Test for Spotting Data Outliers

The Grubbs test is a statistical way to spot a possible outlier in a data set. It is used when the data is assumed to follow a normal distribution. A Grubbs test helps decide whether one unusual numerical value should be treated as a true outlier. To run a two-tailed Grubbs test, first find the absolute difference between the questionable value and the mean. Then divide that difference by the sample standard deviation. The result is the Grubbs statistic, or G. Next, compare the calculated G...

what is anova?

ANOVA for Comparing Sample Means

ANOVA, or Analysis of Variance, is a statistical test used to compare the means of three or more samples. It was developed by Ronald Fisher in 1918. In high school statistics, ANOVA helps determine whether group averages are equal or whether at least one group is different. Before ANOVA is used, the samples should meet three key assumptions. The data should come from normally distributed samples. The samples should also be randomly and independently selected. In addition, the populations...

key terms and concepts

Explore how isotope ratios affect atomic mass, why systematic uncertainty follows a rectangular distribution, and how atomic uncertainties add into molecular mass.

Atomic Mass Uncertainty in Isotope Samples | 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。

analytical chemistry

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physical-and-chemical-properties-of-matter

Physical and Chemical Properties of Matter

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CHEM.2.A

Types of Errors: Detection and Minimization

Error is the deflection of an obtained result from the expected or true results of an experiment. This happens due to the uncertainty associated with the ...

Systematic Error: Methodological and Sampling Errors

Systematic errors, depending on their source, are of four types &#8211; sampling, instrumental, method, and personal errors.
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Random Error

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Since these errors can neither be predicted nor ...

Uncertainty: Overview

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However, uncertainty varies from error, which is the ...

Propagation of Uncertainty from Random Error

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The atomic mass of an element obtained from different sources changes slightly due to the variation in relative isotope concentration from one source to ...

SI Units: 2019 Redefinition

Measurement is integral to analytical chemistry. Each record comprises a number &#8211; denoting the magnitude&#160; &#8211; and the unit &#8211; a ...

Degrees of Freedom

The degree of freedom is the number of independent pieces of information or sample values required to perform any calculation.
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Statistical Analysis: Overview

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Standard Deviation of Calculated Results

A plot of relative deviation from the mean and its frequency of occurrence appears as a Gaussian curve. This probability distribution curve of a ...

Introduction to z Scores

The z score, or standardized score, is the number of standard deviations that a given value is away from the mean. It is one of the commonly used measures ...

Uncertainty: Confidence Intervals

Standard deviation provides a measure of nearness between the sample mean and the true mean reliably for a large number of measurements.
So, when there ...

Significance Testing: Overview

Is the difference between the two values due to an unexplainable random error or a systematic error that can be rationalized by a hypothetical model?
The ...

Identifying Statistically Significant Differences: The F-Test

The F-test checks if the difference between two variances is too large to be explained by an indeterminate error. It compares the variance of a sample and ...

Comparing Experimental Results: Student's t-Test

The influence of changing the method, the sample, or the analyst on the analysis results is studied by altering only one in a pair of experiments.
The ...

Detection of Gross Error: The Q Test

Outliers are those data points extremely different from the rest of the data set.
Dixon&#39;s Q-test is a significance test that helps determine whether ...

Correlation and Regression

Regression and correlation are statistical techniques that examine the relationship between two variables.&#160;
While regression is used to understand ...

Difference from Background: Limit of Detection

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Quantifying and Rejecting Outliers: The Grubbs Test

Grubbs&#39; test, like Dixon&#39;s Q-test, is a statistical test to identify the outliers in data with a normal distribution. Here, the number of ...

What is ANOVA?

The analysis of variance&#8212;abbreviated as ANOVA &#8212;is used when the means of three or more samples need to be tested for equality.
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CHEM.2.C

Scientific and engineering practices. The student analyzes and interprets data to derive meaning, identify features and patterns, and discover relationships or correlations to develop evidence-based arguments or evaluate designs. The student is expected to:

Atomic Mass Uncertainty in Isotope Samples

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