1. IB BIOLOGY INTERNAL ASSESSMENT
ANALYSIS #1
Miss Werba / Mr Hyde

2. PRAC REPORTS
Everything as before, with a little more and a slightly different focus.
EXPLORATION Background Info / Introduction (with evidence of personal engagement)
ANALYSIS Data Collection (raw data)
Data processing (with calculations, uncertainties, tables & graphs)
Research question (with variables clearly identified)
Discussion
Hypothesis
EVALUATION Evaluation of methodology (with strengths, weaknesses and limitations)
Apparatus/materials
Method (with the process itself, the control of variables and the safety concerns all addressed)
Suggested improvements & extension
Conclusion

3. What should be included?
Raw data
Intro to processing
Equations (with sample calculations)
Tables
Graphs
Statistical analysis – essential
Error analysis – optional
Outlier analysis – optional
t-Test – optional
Explanation of significance of data

4. MATHEMTICAL REQUIREMENTS OF THE IB
You must be able to:
Perform basic arithmetic functions
Carry out calculations involving means, decimals, fractions, percentages & ratios
Represent and interpret frequency data in the form of bar charts, graphs & histograms
Plot graphs (with suitable scales and axes)
Plot and interpret scattergraphs to identify a correlation between two variables
Determine the mode and median of a data set
Calculate and analyse the standard deviation
Select statistical tests appropriate for the analysis of particular data and interpret the results.

5. Data collection
Must include all raw data, as recorded on the day of the investigation
No calculations here!
Data must be both:
Qualitative – observations, drawings, pictures
Quantitative – numbers
Data should be presented appropriately

6. Data collection
Uncertainties (or the precision of your measurements) should be included for your results:
Shown as a ± in the column heading of your tables (if it’s consistent) or after each measurement (if it’s variable).
It is generally the half of the smallest unit of measurement on the device or a subjective figure that you have determined.
Units should be on all data:
Shown in the column headings also
Not in body of table!
Use a standard number of decimal places or significant figures! Be consistent with the precision of your equipment and the rest of your data!
Centre-align all data

7. DV (units ± error with units)
Data collection
All tables/diagrams/drawings should have a suitable, numbered heading.
Table headings are included above the object and are underlined.
Picture/diagram/graph headings are included below the object.
The IV of the experiment should go on the left-hand side of the table
The DV should go along the top or on the right-hand side.
Table 1. Data collecting showing the DV when the IV was increased. IV
(with units)
DV (units ± error with units)
Trial 1
Trial 2
Condition 1
Condition 2

8. Data processing Introduce each step in your analysis
Provide the formula used and a sample calculation before presenting the remainder of the processed data in a table.
Examples:
calculating the difference between two data sets
calculating the mean and standard deviation of a data set
calculating a rate of reaction from the data set
calculating the range of a data set
calculating the correlation (R value) between two variables
calculating the reliability of your data by identifying outliers
calculating cumulative and percentage errors
calculating the statistical significance of your data

9. Data processing Data should be presented appropriately
Again, you need to provide numbered, detailed headings.
Graph your processed data:
Do not graph raw data!
Choose an appropriate graph!!! Depends on the data (continuous or discontinuous data, numerical vs categorical)
The graphs are there to make comparisons easier between 2 or more data sets.
Multiple trials can be grouped together and a number of factors can be compared.
Axes must have titles (including units and uncertainties)
Correct scales are needed
The IV should be on the horizontal axis and the DV on the vertical axis.
Always opt for a scatterplot with line of best fit rather than a line graph
Error bars are essential, usually showing the standard deviation of the data or the range.

10. Data processing - example
Calculating the volume of gas produced
In order to calculate the volume of gas produced, the difference between the initial and final readings for each trial need to be determined.
A sample calculation has been provided for Trial 1 of the Sand condition:
𝑽𝒐𝒍𝒖𝒎𝒆 𝒐𝒇 𝒐𝒙𝒚𝒈𝒆𝒏 𝒑𝒓𝒐𝒅𝒖𝒄𝒆𝒅 𝒄𝒎 𝟑 =𝒇𝒊𝒏𝒂𝒍 𝒗𝒐𝒍𝒖𝒎𝒆 −𝒊𝒏𝒊𝒕𝒊𝒂𝒍 𝒗𝒐𝒍𝒖𝒎𝒆
=𝟐𝟔−𝟐𝟐
=4 𝑐𝑚 3 (± 0.2 𝑐𝑚 3 )
This data has been presented in Table 2.

11. Data processing - example
Table 2: The volume of oxygen (cm3) produced in each trial, as determined by the difference between the final and initial volumes of water in the measuring cylinder.
Volume of oxygen produced (cm3 ± 0.2cm3)
Substrate
Trial 1
Trial 2
Trial 3
Trial 4
Trial 5
Sand 4 1 3
MnO2 32 14 18 40 11
Liver 27 9 25
Cabbage 5
Potato 2

12. Data processing - example
Table 3: The mean volume of oxygen gas (cm3) produced in each condition.
Substrate
Mean volume of oxygen produced
(cm3 ± 1cm3)
Sand
1.6
MnO2
23.0
Liver
18.0
Cabbage
4.0
Potato
3.0

13. Data processing - example
Figure 1: The mean volume of oxygen gas (cm3) produced in each condition. Error bars represent the uncertainty in the instrumental measuring device used.

14. ANALySIS Mark Descriptor 6
Aspect 1: The report includes sufficient relevant quantitative and qualitative raw data that could support a detailed and valid conclusion to the research question.
Aspect 2: Appropriate and sufficient data processing is carried out with the accuracy required to enable a conclusion to the research question to be drawn that is fully consistent with the experimental data.
Aspect 3: The report shows evidence of full and appropriate consideration of the impact of measurement uncertainty on the analysis.
Aspect 4: The processed data is correctly interpreted so that a completely valid and detailed conclusion to the research question can be deduced.

15. Other handy dandy hints...
Always ask for help if you are unsure!
Don’t stress out if your data doesn’t tell you anything or is statistically unreliable – gives you something to talk about!
The more thought and care that you put into your analysis, the more reliable your data will be and the more you will have to talk about in the later sections of your report!

16. AND NOW FOR YOUR DATA  TOO
Depending on what variables you have chosen, you will need to filter and interpret the raw data.
The easiest thing to do is find an average. But anyone could do that…you need to try to prove that there is a high certainty that your manipulation of one variable was closely linked to the change in another.
TOO

17. AND NOW FOR YOUR DATA  NOT SO ? I would suggest looking at:
- the average
- the standard deviation
- the range
- outlier analysis (then recalculate the mean, sd and range)
- the t-test value (for statistical significance)
NOT SO ?

18. NEXT STEP Don’t forget to include the qualitative data as well!
We will work through the calculations using MS Excel and you will need to put together your Analysis section until you reach a reasonable answer to your Research Question using your data.