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Topic 11: Measurement and Data Processing


11.1 Uncertainties and Errors in Measurement and Results
Essential Idea:
  • All measurement has a limit of precision and accuracy, and this must be taken into account when evaluating experimental results.
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 & 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.
Nature of Science:
  • Making quantitative measurements with replicates to ensure reliability—precision, accuracy, systematic, and random errors must be interpreted through replication.
Video: Propogating Uncertainties
Video Lesson: Random vs. Systematic Error
Review Significant Figures & Math in Chemistry
Video: Significant Figures in Measurements
Extra Practice: Recording Data to Correct Sign. Figures
Lab Data Practice Workbook
Percent Error Rap
11.2 Graphical Techniques
Essential Idea:
  • Graphs are a visual representation of trends in data.
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 & 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.
Nature of Science:
  • The idea of correlation—can be tested in experiments whose results can be displayed graphically.
Video Guide: Beginners Guide to Graphing
Extra Practice: Making & Interpretting Graphs
Khan Academy: Calculating Slope from Graph
Video: Making a Good IB Graph Using Excel
11.3 Spectroscopic Identification of Organic Compounds
Essential Idea:
  • Analytical techniques can be used to determine the structure of a compound, analyse the composition of a substance or determine the purity of a compound. Spectroscopic techniques are used in the structural identification of organic and inorganic compounds.
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 & 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.
Nature of Science:
  • Improvements in instrumentation—mass spectrometry, proton nuclear magnetic resonance and infrared spectroscopy have made identification and structural determination of compounds routine. (1.8) Models are developed to explain certain phenomena that may not be observable—for example, spectra are based on the bond vibration model.
Video: How does a Mass Spectrometer Work?
Video: Introduction to proton NMR
Video: A Simple Introduction to IR Spectroscopy
Determination of IHD from molecular formula