1. Data Collection and Processing (DCP) 1

2. Key Aspects (1) DCPRecording Raw Data Processing Raw Data Presenting Processed Data CompleteRecords appropriate quantitative and associated data, including units and uncertainties where relevant Processes the quantitative raw data correctly Presents processed data appropriately and, where relevant, includes errors and uncertainties You are marked on 3 components of data collection and processing (DCP): recording, processing and presenting 2

3. Key aspects of DCP(2) 1. Record data appropriately, noting uncertainties 2. Process correctly (descriptive statistics) 3. Present appropriately, including uncertainties YOU must decide on the relevant data to be collected, and the range over which it will be collected YOU must be able to draw your own data tables and graphs 3

4. Raw Data Quantitative If data is extensive (e.g. Data Logger data), it may be placed in an appendix at the end of the report, such that the results section only includes the summary. If raw data is brief it should all be included in the Results section. Record data in a table which includes units and uncertainties (apparatus accuracy) Qualitative Mention observations Record personal uncertainties and attempt to quantify them 4

5. To gain a complete for raw data presentation: Data must be individually collected (though can be processed as class data) Data must be sufficient to require a reasonably complex table Uncertainties must be included in table headings, graph axes etc Processing must be individual, justified, complete and appropriate 5

6. Significant figures Should be: At, and not beyond, the uncertainty value of the instrument Consistent in terms of the decimal places At the same level of accuracy in the processed data Example: Ruler measurement of 3.5 mm, will have uncertainty of ± 0.05 (mathematically)/ ± scientifically, limit of instrument 6

7. Tabulating Raw Data Use p. 10 – 12 in your IB Biology Student Guide for Internal Assessment 7

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9. Inadequate Raw data table 9

10. Period D: Egg Experiment, Group 1 data 10

11. Period E: Sweet potato experiment, Group 1 data 11

12. Uncertainties – limitations of measurements Biological uncertainties – associated with natural variation Human errors (mistakes): systematic or random Instrument uncertainties (absolute or systematic) Inappropriate technique Anomalous results 12

13. Biological Uncertainties There is natural biological variation between individual specimens. Inevitable biological uncertainty can be minimised by: Having a large sample Random sampling Select similar/ uniform organisms or biological material Ensure sufficient repeats 13

14. Instrument Uncertainties (1) There is a sensible limit as to how accurate a measurement needs to be The uncertainty must be based on what is being measured Always use the most accurate apparatus available, and use it carefully and precisely 14

15. Instrument Uncertainties (2) Instrument uncertainty must be recorded in the raw data table The number of significant figures matches with the uncertainty: 7.55 cm ± 0.5 cm is wrong 7.50 cm ± 0.05 cm is correct Standard deviation can be used if the sample size is sufficient 15

16. Estimated (Personal) uncertainties Collecting observational data using live organisms has intrinsic limits to its precision. In such cases you should make a sensible estimated uncertainty. E.g.1: monitored gill movements of a fish: uncertainty ± 1 bpm E.g. 2: abdominal movements of a locust: uncertainty ± 2 bpm 16

17. Anomalous Results Anomalous: adjective: deviating from what is normal, standard or expected These are values which don’t fit the general pattern or trend, or don’t fit the predicted line on a graph. These values should be included in the raw data table, marked with an asterisk, and NOT included in data processing The anomalous result should be included in the EVALUATION section. 17

18. Component 2 of DCP: Data Processing Refer to and use P. 14 –19 Numerical processing – use of formulae and calculations Simple descriptive statistics – mean, median, mode, standard deviation, standard error, assessment of normal distribution (confidence interval). Simple statistical techniques: Student’s t-test, logistic regression, Chi-squared analysis, Mann-Whitney U- test, Wilcoxon Test 18

19. Numerical Processing of Raw Data You must state and give an example of the formula used for basic calculations. Your formula, and the calculation itself must be clear Include units and maintain a uniform number of significant figures Use of a table improves clarity 19

20. Numerical Processing of Raw Data To estimate the rate of osmosis across the egg cell membrane (or the sweet potato cell membrane):  Calculate the % change in mass using the formula: % change in mass = [initial mass – final mass / initial mass ] X 100 For an egg with initial mass of 75.45 g and final mass of 82.30 g (± 0.01g), % change in mass = (75.45g – 82.30g)/75.45g X 100 = + 9.1% 20

21. Numerical Processing of Raw Data To estimate the rate of osmosis across the egg cell membrane (or the sweet potato cell membrane): RATE is how fast something is happening per unit time. NB: Rate can be calculated from the slope of appropriately graphed data Use the formula: rate of osmosis = [( % change in mass / duration of experiment) %h -1 = (9.1% / 24h) X 100 = 0.38%h -1 21

22. Period E data processing 22

23. Period D Data processing 23

24. Basic descriptive statistics i-biology statistical links 24

25. Basic Descriptive statistics: Standard Deviation Standard Deviation is measure of the variability (spread) in a set of data In a normally distributed data set, 68% of all data values will fall inside one s of the mean, and 95% of all data within 2 standard deviations of the mean. 25

26. Basic Descriptive statistics: Standard Error 26 Another simple way to describe variability in normally distributed data

27. Aspect 3 of DCP: Graphical presentation P. 20 – 32 of Student Guide for Internal Assessment 27

28. Graphs used for data presentation 1. Line graph (most common) 2. Scatter graph (Excel: X-Y scatter – commonly used to make line graphs!!!) 3. Bar graph (Excel: Column or Bar) 4. Histogram (Excel: Column/clustered) 5. Pie Chart 6. Kite diagram 28

29. Selecting the right type of graph Use a line graph when your experiment employed independent and dependent variables Use a scatter graph when you are looking for a potential correlation between two sets of data, neither of which was manipulated Use a bar graph when there is no relationship between the bars and thus a gap between them Use a histogram when each bar is directly related to the bars on either side, and thus illustrates a distribution pattern Pie charts are used to show a proportion of the whole (ecology) Kite diagrams are used to show distibution across a region (ecology) 29

30. Processed data is plotted on the graph Independent variable is always on the X-axis Dependent data is always on the Y-axis Typically, you will plot mean/median data and also include uncertainty The axes should be exactly labelled with the same titles used for the column headings The graph must be titled fully and precisely 30

31. Data collection and processing Process the uncertainties involved to give an uncertainty for each measurement and an overall estimate of uncertainty. The same degree of accuracy should be used for all data. Draw carefully labeled, relevant diagrams, graphs, pictograms including error bars where possible. Ensure that labels are clear, correct and include units and uncertainties. Process relevant data from graphs or diagrams e.g. gradient, percentage cover. Statistically analyse the data if relevant e.g. means, standard distributions, t-test. 31

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