1. TIMETABLE LAYOUT Lecture 2: Working in the Laboratory Electricity and Measurement (E&M)BPM – 15PHF110

2. In This Lecture… 1. Objectives Objectives 2. Using Laboratory Note Books Using Laboratory Note Books 3. Lab Work Assessment for PHF110 Lab Work Assessment for PHF110 4. Measuring Quantities Measuring Quantities 5. Uncertainties in Measurements Uncertainties in Measurements 6. Plotting Graphs Plotting Graphs

3. Objectives  Know how to effectively maintain an experimental log book.  Know the assessment structure for lab work in this module.  Explain what is meant by the term “experimental uncertainty”.  Understand the difference between standard uncertainty and relative uncertainty.  Distinguish between random uncertainty and systematic uncertainty and recognise their sources.  Describe how to record data so that random and systematic uncertainty is accounted for and their impact reduced.  Construct graphs to present data effectively, including graphical treatment of the uncertainties in the plotted data.

4. The laboratory notebook is the principal tool of a scientist: maintain it professionally. Laboratory Notebook Record your work using neat ink only The notebook is a working document, in the lab, The pen you use needs to be rugged, this means, hard wearing does not fade with time or exposure does not smudge does not run when wet  Do not use pencil except for graphs and diagrams. on the work desk,amongst all the equipment. Pentel Hybrid Gel Grip Rollerball Black ⟶ Sakura Gelly Roll Pen Fine Black ⟶

5. Record your work using neat ink only Permanently attach loose pieces of paper, e.g. extra graphs, etc. Cross out mistakes neatly. Laboratory Notebook  Do not use pencil except for graphs and diagrams.  Do not tear out pages from the notebook  Do not use corrective fluid. like this not like that The laboratory notebook is the principal tool of a scientist: maintain it professionally.

6. Laboratory Notebook Record your work using neat ink only Permanently attach loose pieces of paper, e.g. extra graphs, etc. Cross out mistakes neatly. Perform calculations in the notebook  Do not use pencil except for graphs and diagrams.  Do not tear out pages from the notebook  Do not use corrective fluid.  Do not use loose paper for records aiming to write them up later. If you have your calculations in your notebook you can easily find them. The laboratory notebook is the principal tool of a scientist: maintain it professionally.

7. Laboratory Notebook Record your work using neat ink only Permanently attach loose pieces of paper, e.g. extra graphs, etc. Cross out mistakes neatly. Perform calculations in the notebook Every piece of data that is recorded must have an estimate of uncertainty. The result of every calculation must have an estimate of uncertainty.  Do not use pencil except for graphs and diagrams.  Do not tear out pages from the notebook  Do not use corrective fluid.  Do not use loose paper for records aiming to write them up later. m = 1.50 ± 0.05 kg m = 1.50 kg  The laboratory notebook is the principal tool of a scientist: maintain it professionally.

8. Laboratory Notebook Record your work using neat ink only Permanently attach loose pieces of paper, e.g. extra graphs, etc. Cross out mistakes neatly. Perform calculations in the notebook Every piece of data that is recorded must have an estimate of uncertainty. The result of every calculation must have an estimate of uncertainty.  Do not use pencil except for graphs and diagrams.  Do not tear out pages from the notebook  Do not use corrective fluid.  Do not use loose paper for records aiming to write them up later.  Do not simply take the measurements with the aim of analysing later because… when you start analysing you often find you need to take more readings or make adjustments to equipment. The laboratory notebook is the principal tool of a scientist: maintain it professionally. when you start analysing you often find you need to take more readings or make adjustments to equipment.

9. Lab Work Assessment

10. Measuring Quantities All measurements are subject to uncertainty. E.g. diameter of a gold ring can fluctuate due to, small fluctuations of temperature from one measurement to another (thermal expansion) non-perfect shape of the ring fluctuations in measurement tools etc. A statement of a measured value without an accompanying estimate of the uncertainty is useless. diameter circumference

11. Single Measurements Never quote insignificant figures, e.g. 88.38  0.1 0 C. This cannot be possible as the accuracy quoted can only be as good as the uncertainty in the measurement. The correct reading of this temperature would be: uncertainty in A 88.4  0.1 0 C 20222426283032 78808284868890 E.g. measuring the temperature of a cooling liquid at a certain time. θ = 25.4  0.1 0 C, in general, A   A The uncertainty defines the number of significant figures that can be quoted, e.g.

12. Different Ways to Quote Uncertainty 1. Standard Uncertainty (Error) – given as a  value. i.e. A   A = 25.4  0.1 0 C 2. Relative Uncertainty (Error) – either given as a fractional or a percentage uncertainty (error). fractionalpercentage Relative Uncertainty Standard Uncertainty The relative uncertainty is found from the standard uncertainty, i.e. For our temperature reading, this gives, relative uncertainty

13. Effects Leading to Uncertainty Random - where repeating the measurement gives a randomly different result. The more measurements made, the better the estimate of the true value. For example, measuring the time of a mass falling from a fixed height, measuring the diameter of a length of wire. Systematic - where the same influence affects the result for each of the repeated measurements, often this is not known as it is out of the control of the experimenter. For example, a cheap ruler that has an incorrectly marked scale, a spring balance whose spring has stiffened with age.

14. Sources of Uncertainty no parallax parallax no parallax Never view at an angle when taking measurements. Move the eye into the correct position perpendicular to the scale. Sources of Random Uncertainty Mistakes – misreading a scale, writing the wrong number down, etc. Not a genuine uncertainty. Parallax error is an example of a mistake.

15. Sometimes there is help to do this: mirror image of needle Sources of Uncertainty

16. Sometimes there is help to do this: Not square on, parallax error: 0.113 Too low Not square on, parallax error: 0.120 Too High Square on, no parallax error: 0.116 Correct reading Sources of Uncertainty

17. Sources of Random Uncertainty Mistakes – misreading a scale, writing the wrong number down, etc. Not a genuine uncertainty. Human Error – reaction time and poor observation techniques. Binning Error – rounding off readings, this depends on the precision of the instrument’s scale. 2.5 ± 0.5 cm = 0.025 ± 0.005 m 24.5 ± 0.5 mm = 0.0245 ± 0.0005 m True value 0.0244 m Sources of Uncertainty

18. Sources of Random Uncertainty Mistakes – misreading a scale, writing the wrong number down, etc. Not a genuine uncertainty. Human Error – reaction time and poor observation techniques. Binning Error – rounding off readings, this depends on the precision of the instrument’s scale. Statistical fluctuation – when taking measurements of a sample from a large population. Random errors are caused by sources that are not immediately obvious, For example changes in temperature, background noise, light levels, etc. can effect the measuring equipment and/or what is being measured. Random errors can be reduced by averaging a large number of readings. This is because the mean value gets closer to the true value as the number of readings increases.

19. Sources of Uncertainty Sources of Random Uncertainty Mistakes – misreading a scale, writing the wrong number down, etc. Not a genuine uncertainty. Human Error – reaction time and poor observation techniques. Binning Error – rounding off readings, this depends on the precision of the instrument’s scale. Statistical fluctuation – when taking measurements of a sample from a large population. Sources of Systematic Uncertainty Instrumental error – calibration is never perfect.

20. Standard measure Good Calibration Poor Calibration Calibration of a RulerZero Error on Force Meter Correct Alignment Zero Error Sources of Uncertainty Instrumental error

21. Sources of Uncertainty Sources of Random Uncertainty Mistakes – misreading a scale, writing the wrong number down, etc. Not a genuine uncertainty. Human Error – reaction time and poor observation techniques. Binning Error – rounding off readings, this depends on the precision of the instrument’s scale. Statistical fluctuation – when taking measurements of a sample from a large population. Sources of Systematic Uncertainty Instrumental error – calibration is never perfect. Error due to Observation – the act of taking the reading changes its value.

22. Cold thermometer into hot liquid will cause the liquid to cool The smaller the observed system is, the bigger the effect This becomes extremely important on atomic scales (observing in quantum physics) Ammeters or voltmeters in a circuit will effect the current Low resistance ammeters and high resistance voltmeters reduce the effect Observation effects the Observed

23. Plotting Graphs A Cartesian graph comprises of two axes (the y-axis or ordinate and the x-axis or abscissa), which cross at the origin. Coordinates are always given in the form (x, y), e.g. × (6, 2) (-4, -8) ×

24. Plotting Graphs The independent variable is usually plotted along the abscissa The dependent variable is usually plotted along the ordinate. Independent variable – the variable whose values were chosen by the researcher before running the experiment Dependent variable – the variable whose values are the readings taken by the researcher during the experiment The phrase: “plot temperature against time” means:

25. Plotting Graphs The uncertainty in the values plotted are shown using error bars.

26. Plotting Graphs A fitted line passes through at least 2/3 of error bars – maximum and minimum gradient lines give estimate of uncertainty in the gradient value. Fitted line Maximum gradient Minimum gradient

27. Plotting Graphs however

28. Objectives  Know how to effectively maintain an experimental log book.  Know the assessment structure for lab work in this module.  Explain what is meant by the term “experimental uncertainty”  Understand the difference between standard uncertainty and relative uncertainty.  Distinguish between random uncertainty and systematic uncertainty and recognise their sources.  Describe how to record data so that random and systematic uncertainty is accounted for and their impact reduced.  Construct graphs to present data effectively, including graphical treatment of the uncertainties in the plotted data.