Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Errors in Chemical Analyses Chapter 5 Skoog, West, Holler and Crouch 8th Edition Slide 1 Usage of the term Error 1. Error: difference between measured value and true value 2. Error: uncertainty in measurement Slide 2 1 Mean and Median • Results of replicate measurements are customarily reported as a mean • Mean: estimate of the central value • Median = middle value In case of an even number of measurements, the average of the central pair in the middle value • When is it advantageous to use median rather than the mean? – In case of gross error Slide 3 Median Mean Qtest Qcrit 791 850 862 865 869 847.4 0.76 0.71 39.25 39.61 39.68 39.83 39.645 Slide 4 2 Accuracy • Acurracy refers to the closeness of the measurement to the true value. • Absolute error E = x i − xt • Relative error %E = xi − xt × 100 xt Slide 5 Precision • Precision refers to the closeness between measurements of a set obtained exactly the same way. • Measures of Precision – Standard deviation – Variance – Coefficient of variance (CV): %RSD • Deviations from the mean – Absolute values Slide 6 3 Accuracy and Precision Slide 7 Fig 5-2, p.93 Accuracy and Precision Standardization of HCl by TRIS Mean M.R. 0.09531 0.09547 0.09530 0.09514 0.09546 M.R. 0.08732 0.08740 0.09094 0.08824 0.08672 M.R. 0.08757 0.08885 0.08795 0.08697 0.08797 M.R. 0.08814 0.08899 0.08828 0.08886 0.08823 0.09536 STD 0.09534 0.00012 0.08812 0.00166 0.08786 0.08786 0.00061 0.08834 0.08847 0.00036 MR indicator range: 6.0 (yellow) - 4.8 (Red) End point: appearance of pink pHeq ~ 4.5 (depends on concentration of TRIS in solution) Slide 8 4 Accuracy and Precision Determination of nitrogen Slide 9 Fig 5-3, p.94 Types of Errors in Experimental Data • 2 main types • Random or indeterminate errors – Are inherent to all measurements – Cause symmetrical spread of data round the mean – Lower precision • Systematic errors – Lower accuracy • (Gross errors – Produce outliers) Slide 10 5 Sources of Systematic errors • Instrumental errors – Examples: • • • Method errors – Examples: • • • faulty calibration temperature effect on electronic equipment use of a “bad” indicator for an acid-base titration presence of an interferent, Personal errors – Example: • • Incorrect reading of a buret bias Slide 11 Effects of Systematic Errors • Systematic errors can be constant or proportional • Constant: the absolute error is independent of the magnitude of the measured quantity, thus of the size of the sample. – Thus the relative error varies – Decreases as the – Example: titration error The color change of a chemical indicator requires an overtitration of 0.04 mL. Calculate the percent relative error if the total volume of the titrant is mL %error 0.08 50.00 25.00 0.16 10.00 0.4 40.00 0.1 Slide 12 6 • Proportional errors increase or decrease proportionally to the size of the quantity measured. – Absolute error varies with size of sample – Relative error is constant – Example: • the amount of an interferent increases with the size of the sample Slide 13 • A proportional systematic error can change the slope while preserving the linearity of the function Slide 14 Fig 8-10, p.195 7 Detection of Systematic Instrument and Personal Errors • All instruments are subject to wear and tear • Periodic calibration is necessary • Personal errors…. Slide 15 Detection of Systematic Method Errors • Analysis of standard samples such as SRMs from NIST. Three approachesused to determine the concentration of analytes of interest in SRMs: – analysis using a previously validated reference method – Analysis by two or more independent, reliable methods – Analyzed by a network of cooperating laboratories • Independent Analysis – Use another available reliable analytical method. – The principle of the method must differ from the method being questioned Slide 16 8 Detection of Systematic Method Errors • Blank Determination – Blank: solvent and reagents – Blank: solvent, reagent and sample matrix – Matrix: everything else in the sample except analyte. A matrix can be very difficult to mimic. • Variation in sample size – Constant error will be detected as a decrease in relative error Slide 17 Recommended Excel exercises • Calculate mean using the SUM and Count functions • Calculate mean using the AVERAGE function Slide 18 9