1. Uncertainty & Errors in Measurement

2. Waterfall by M.C. Escher

3. Keywords Uncertainty Precision Accuracy Systematic errors Random errors Repeatable Reproducible Outliers

4. Measurements = Errors Measurements are done directly by humans or with the help of Humans are behind the development of instruments, thus there will always be associated with all instrumentation, no matter how precise that instrument is.

5. Uncertainty When a physical quantity is taken, the uncertainty should be stated. Example If the balance is accurate to +/- 0.001g, the measurement is 45.310g If the balance is accurate to +/- 0.01g, the measurement is 45.31g

6. Exercise A reward is given for a missing diamond, which has a reported mass of 9.92 +/- 0.05g. You find a diamond and measure its mass as 10.1 +/- 0.2g. Could this be the missing diamond?

7. Significant Figures (1) ____ significant figures in 62cm 3 (2) ____ significant figures in 100.00 g. MeasurementsSig. Fig.MeasurementsSig. Fig. 1000sUnspecified0.45 mol dm -3 2 1 x 10 3 s14.5 x 10 -1 s mol dm -3 2 1.0 x 10 3 s24.50 x 10 -1 s mol dm -3 3 1.00 x 10 3 s34.500 x 10 -1 s mol dm -3 4 1.000 x 10 3 s44.5000 x 10 -1 s mol dm -3 5 The 0s are significant in (2) What is the uncertainty range?

8. Random (Precision) Errors An error that can based on individual interpretation. Often, the error is the result of mistakes or errors. Random error is not ______ and can fluctuate up or down. The smaller your random error is, the greater your ___________ is.

9. Random Errors are caused by The readability of the measuring instrument. The effects of changes in the surroundings such as temperature variations and air currents. Insufficient data. The observer misinterpreting the reading.

10. Minimizing Random Errors By repeating measurements. If the same person duplicates the experiment with the same results, the results are repeatable. If several persons duplicate the results, they are reproducible.

11. 10 readings of room temperature 19.9, 20.2, 20.0, 20.0, 20.1, 19.9, 20.3, 19.9, 20.2, 22.3 (a) What is the mean temperature? The temperature is reported as as it has a range of Read example in the notes.

12. Systematic Errors An error that has a fixed margin, thus producing a result that differs from the true value by a fixed amount. These errors occur as a result of poor experimental design or procedure. They cannot be reduced by repeating the experiment.

13. 10 readings of room temperature 20.0, 20.3, 20.1, 20.1, 20.2, 20.0, 20.4, 20.0, 20.3 All the values are ____________. (a) What is the mean temperature? The temperature is reported as 19.9, 20.2, 20.0, 20.0, 20.1, 19.9, 20.3, 19.9, 20.2

14. Examples of Systematic Errors Measuring the volume of water from the top of the meniscus rather than the bottom will lead to volumes which are too ________. Heat losses in an exothermic reaction will lead to ______ temperature changes. Overshooting the volume of a liquid delivered in a titration will lead to volumes which are too ______.

15. Minimizing Systematic Errors Control the variables in your lab. Design a “perfect” procedure ( not ever realistic)

16. Errors Systematic errors Apparatus Way in which readings are taken Random errors Equal chance of reading being high or low from 1 measurement to the next

17. How trustworthy is your reading? Accuracy How close to the accepted(true) value your reading is. Precision The reproducibility of your reading Reproducibility does not guarantee accuracy. It could simply mean you have a very determinate systematic error.

18. If all the temperature reading is 20 0 C but the true reading is 19 0 C. This gives us a precise but inaccurate reading. If you have consistently obtained a reading of 20 0 C in five trials. This could mean that your thermometer has a large systematic error. systematic error accuracy random error precision

19. systematic error accuracy random error precision

20. Exercise

21. Putting it together Example The accurate pH for pure water is 7.00 at 25 0 C. Scenario I You consistently obtain a pH reading of 6.45 +/- 0.05 Accuracy: Precision:

22. Scenario II You consistently obtain a pH reading of 8 +/-2 Accuracy: Precision:

23. Calculations involving addition & subtraction When adding and subtracting, the final result should be reported to the same number of decimal places as the least no. of decimal places. Example: (a) 35.52 + 10.3 (b) 3.56 – 0.021

24. Calculations involving multiplication & division When adding and subtracting, the final result should be reported to the same number of significant figures as the least no. of significant figures. Example: (a) 6.26 x 5.8 (b)

25. Example When the temperature of 0.125kg of water is increased by 7.2 0 C. Find the heat required. Heat required = mass of water x specific heat capacity x temperature rise = 0.125 kg x 4.18 kJ kg -1 0 C -1 x 7.2 0 C = Since the temperature recorded only has 2 sig fig, the answer should be written as ____________

26. Multiple math operations Example:

27. Uncertainties in calculated results These uncertainties may be estimated by from the smallest division from a scale from the last significant figure in a digital measurement from data provided by the manufacturer

28. Absolute & Percentage Uncertainty Consider measuring 25.0cm 3 with a pipette that measures to +/- 0.1 cm 3. We write Absolute Uncertainty Percentage Uncertainty The uncertainties are themselves approximate and are generally not reported to more than 1 significant fgure.

29. Percentage Uncertainty & Percentage Error

30. When adding or subtracting measurement, add the absolute uncertainties Example Initial temperature = 34.50 0 C Final temperature = 45.21 0 C Change in temperature, ΔH

31. When multiplying or dividing measurement, add the percentage uncertainties Example Given that mass = 9.24 g and volume = 14.1 cm 3 What is the density?

32. Example Calculate the following: (a) (b)

33. Example: When using a burette, you subtract the initial volume from the final volume. The volume delivered is Final vol = 38.46 Initial vol = 12.15 What is total volume delivered?

34. Example The concentration of a solution of hydrochloric acid = moldm -3 and the volume = cm 3. Calculate the number of moles and give the absolute uncertainty.

35. When multiplying or dividing by a pure number, multiply or divide the uncertainty by that number Example

36. Powers : When raising to the nth power, multiply the percentage uncertainty by n. When extracting the nth root, divide the percentage uncertainty by n. Example

37. Averaging : repeated measurements can lead to an average value for a calculated quantity. Example Average ΔH =[+100kJmol -1 ( 10%)+110kJmol -1 ( 10%)+ 108kJmol -1 ( 10%)] 3 = 106kJmol -1 ( 10%)]

38. Calculations Add & Subtract No. of decimal places Multiply & Divide No. of significant figures No. with the fewest sig fig used determines the sig fig to be used for the answer.

39. Factory made thermometers Assume that the liquid in the thermometer is calibrated by taking the melting point at 0 0 C and boiling point at 100 0 C (1.01kPa). If the factory made a mistake, the reading will be biased.

40. Instruments have measuring scale identified and also the tolerance. Manufacturers claim that the thermometer reads from -10 0 C to 110 0 C with uncertainty +/- 0.2 0 C. Upon trust, we can reasonably state the room temperature is 20.1 0 C +/- 0.2 0 C.

41. Graphical Technique y-axis : values of dependent variable x-axis : values of independent variables

42. Plotting Graphs Give the graph a title. Label the axes with both quantities and units. Use sensible linear scales – no uneven jumps. Plot all the points correctly. A line of best fit should be drawn clearly. It does not have to pass all the points but should show the general trend. Identify the points which do not agree with the general trend.

43. Line of Best Equation Temperature ( 0 C)Volume of Gas (cm 3 ) 20.060.0 30.063.0 40.064.0 50.067.0 60.068.0 70.072.0

44. Graphs can be useful to us in predicting values. Interpolation – determining an unknown value within the limits of the values already measured. Extrapolation – requires extending the graph to determine an unknown value that lies outside the range of the values measured.