11. Measurement and data processing
11. Measurement and data processing
11.1 Uncertainties and errors in measurement and results
Nature of science:
Making quantitative measurements with replicates to ensure reliability - precision, accuracy, systematic, and random errors must be interpreted through replication.
Understandings:
Qualitative data includes all non-numerical information obtained from observations not from measurement.
Quantitative data are obtained from measurements, and are always associated with random errors/uncertainties, determined by the apparatus, and by human limitations such as reaction times.
Propagation of random errors in data processing shows the impact of the uncertainties on the final result.
Experimental design and procedure usually lead to systematic errors in measurement, which cause a deviation in a particular direction.
Repeat trials and measurements will reduce random errors but not systematic errors.
Applications and skills:
Distinction between random errors and systematic errors.
Record uncertainties in all measurements as a range (±) to an appropriate precision.
Discussion of ways to reduce uncertainties in an experiment.
Propagation of uncertainties in processed data, including the use of percentage uncertainties.
Discussion of systematic errors in all experimental work, their impact on the results and how they can be reduced.
Estimation of whether a particular source of error is likely to have a major or minor effect on the final result.
Calculation of percentage error when the experimental result can be compared with a theoretical or accepted result.
Distinction between accuracy and precision in evaluating results.
11.2 Graphical techniques
Nature of science:
The idea of correlation - can be tested in experiments whose results can be displayed graphically.
Understandings:
Graphical techniques are an effective means of communicating the effect of an independent variable on a dependent variable, and can lead to determination of physical quantities.
Sketched graphs have labelled but unscaled axes, and are used to show qualitative trends, such as variables that are proportional or inversely proportional.
Drawn graphs have labelled and scaled axes, and are used in quantitative measurements.
Applications and skills:
Drawing graphs of experimental results including the correct choice of axes and scale.
Interpretation of graphs in terms of the relationships of dependent and independent variables.
Production and interpretation of best-fit lines or curves through data points, including an assessment of when it can and cannot be considered as a linear function.
Calculation of quantities from graphs by measuring slope (gradient) and intercept, including appropriate units.
11.3 Spectroscopic identification of organic compounds
Nature of science:
Improvements in instrumentation - mass spectrometry, proton nuclear magnetic resonance and infrared spectroscopy have made identification and structural determination of compounds routine.
Models are developed to explain certain phenomena that may not be observable—for example, spectra are based on the bond vibration model.
Understandings:
The degree of unsaturation or index of hydrogen deficiency (IHD) can be used to determine from a molecular formula the number of rings or multiple bonds in a molecule.
Mass spectrometry (MS), proton nuclear magnetic resonance spectroscopy (¹H NMR) and infrared spectroscopy (IR) are techniques that can be used to help identify compounds and to determine their structure.
Applications and skills:
Determination of the IHD from a molecular formula.
Deduction of information about the structural features of a compound from percentage composition data, MS, ¹H NMR or IR.
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