ACSI 2022 HL Measurement and Data Processing (Teacher) (1)

IBDP Chemistry HL/ Measurement and Data Processing Page 1 Anglo − Chinese School (Independent) Year 5 (2022) IBDP Chemistry HL (IBDP syllabus Topic 11) 11.1 Uncertainties and errors in measurement and results (SL and HL) - Essential Idea: all measurement has a limit of precision and accuracy, and this must be taken into account when evaluating experimental results 11.2 Graphical techniques (SL and HL) - Graphs are a visual representation of trends in data TOPIC 11 MEASUREMENT AND DATA PROCESSING TEACHER COPY – WITH SUGGESTED SOLUTIONS

IBDP Chemistry HL/Measurement and Data Processing Page 2 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. (3.2, 3.4) 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. Guidance: • The number of significant figures in a result is based on the figures given in the data. When adding or subtracting, the final answer should be given to the least number of decimal places. When multiplying or dividing the final answer is given to the least number of significant figures. • Note that the data value must be recorded to the same precision as the random error. • SI units should be used throughout the programme. International–mindedness: • As a result of collaboration