Data Collection and Processing (DCP)

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Key Aspects (1)

DCP Recording Raw Data

Processing Raw Data

Presenting Processed Data

Complete Records 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

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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

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Raw DataQuantitative

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

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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

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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

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Tabulating Raw Data

Use p. 10 – 12 in your IB Biology Student Guide for

Internal Assessment

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Inadequate Raw data table

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Period D: Egg Experiment, Group 1

data

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Period E: Sweet potato experiment, Group 1 data

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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

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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

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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

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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 wrong7.50 cm ± 0.05 cm is correctStandard deviation can be used if the sample size is sufficient

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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

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Anomalous ResultsAnomalous: 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.

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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

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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

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Numerical Processing of Raw Data

To estimate the rate of osmosis across the egg cell membrane (or the sweet potato cell membrane):

1.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

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

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Period E data processing

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Period D Data processing

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Basic descriptive statistics

i-biology statistical links

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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.

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Basic Descriptive statistics: Standard

Error

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Another simple way to describe variability in normally distributed data

Aspect 3 of DCP: Graphical presentation

P. 20 – 32 of Student Guide for Internal Assessment

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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

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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)

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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

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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.

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