1. IB Internal Assessment

2. ANALYSIS (AN) Collect and organize your raw data
Process your raw data appropriately and correctly
If your exploration method did not include sufficient raw data, you cannot earn full marks in this section either. 
Present your processed data completely and appropriately

3. ANALYSIS (AN): cont.
Include 1 sample calculation for each type of calculation
Examine processed data and discuss the range of data
Interpret processed data correctly
What does the data “say?” Do NOT explain why at this point; that is part of the “Conclusion.”

4. What is “raw” data?
Quantitative & Qualitative data that you directly collect during the lab (BEFORE any math is done)
Mass
Volume
Temperature
Length/Height
Observations (qualitative)
The data you collect (or a probe collects) while standing at the lab bench is raw data.

5. What needs to be included when recording raw data?
Completely titled Data Table
Columns & rows correctly and completely labeled
Observations
Level of equipment uncertainty
Level of precision in recorded data remains constant (same number of decimal places)

6. Title of Data Table; must be…
Numbered
Table 1:
Descriptive: includes both DV & IDV as well as detail
Table 1: Initial & Final Mass of a Dialysis Tube Containing Five Different Concentrations of Sucrose Solution When Immersed for 20 Minutes

7. Columns & rows completely labeled; must have…
Complete label for column (or row)
Correct  Concentration of Sucrose Solution
Incorrect  Concentration
Incorrect  Concentration of Solution
Incorrect  Solution Concentration
Incorrect  Molarity of Sucrose Solution
Units!!
(M) for Molarity
Always use metric system (no “pounds” or “inches”)
If Data table goes onto a 2nd page, you must include complete column headings again

8. Concentration of Sucrose Solution (M) Initial Mass (+/- 0.01g)
Page 1
Concentration of Sucrose Solution (M)
Initial Mass
(+/- 0.01g)
Final mass
0.2
Page 2
Concentration of Sucrose Solution (M)
Initial Mass
(+/- 0.01g)
Final mass
0.4
0.6
0.8
1.0

9. Highlight the top row of the table and then click on “repeat header rows” under Table Tools; Layout
If your data table geos onto another page, it will repeat the header row even if you re-format your lab.

10. Concentration of Sucrose Solution (M)
Initial Mass
(+/- 0.01g)
Final mass
0.2
0.4
0.6
0.8
1.0
NOTICE!! The units are ONLY at the top next to the label. Units do NOT go next to the data (#) being recorded.

11. Observations; must be…
Detailed
If recording data over time (ex: each day for a week), then you will have specific observations every day
Important for your conclusion! (for example, may find source of error)
If you state an error like this in the conclusion, it must be in observations.
Descriptive
Be specific as to what you see but do not draw conclusions here
Ex: some are yellow vs. 4 are yellow
Ex: the plant looks unhealthy vs. the leaves on the corn stalk have yellow spots on them

12. Concentration of Sucrose Solution (M) Height of Plant (+/- 0.1cm)
Initial Mass
(+/- 0.01g)
Final mass
Observations
Not sticky; bag has resistance; water dripping from string
0.2
Etc.
0.4
0.6
0.8
1.0
Very sticky; bag looks more wrinkly
Example 1
Example 2
Date
Numbers of Days Passed
Height of Plant (+/- 0.1cm)
Observations
9/6/12
1.3
3 leaves (all green); stem straight
9/7/12 1
1.4
3 leaves (2 all green & 1 has a small brown spot); stem straight
9/8/12 2
3 leaves (2 all green & 1 has a small brown spot); stem straight; 2 small gnats flying around
9/9/12 3
1.6
A 4th leaf has sprouted; gnats not visible today
9/10/12 4
1.7
4th leaf green and the 1 brown spot is bigger today (2 mm diameter)
9/13/12 7
2.1
2nd stem beginning to branch out; leaves are the same

13. Equipment uncertainty
IB Bio is different for error than IB Chemistry (yea!)
IB Bio only requires that you look at the equipment you are using when collecting data; list the uncertainty for that equipment only (degree of precision is ± the smallest division on the instrument)
Ex for a scale: if the scale measures to the hundredths place, the equip. uncertainty is +/- 0.01g (can be found on bottom of scale)
0.005g error for scale g error when massing an object = .01g
Ex for ruler: If measuring in centimeters  +/- 0.1cm
Do NOT list for anything the teacher provides (example- if I make a solution for you, do not include uncertainty of graduated cylinder I used)
List that information in the column headings of your raw data table

14. #.#g (+/- 0.1g) 56 mL (+/- 0.5mL) *you can estimate to 0.5mL
0.05g error for scale g error when
massing an object = .1g
56 mL (+/- 0.5mL)
*you can estimate to 0.5mL
#.##g (+/- 0.01g)

15. reading ± the smallest division on the measuring instrument
RULER:
reading ± the smallest division on the measuring instrument
LIVING THINGS: If you’re counting the number of organisms (# bacterial colonies, # of trees in an area), then you will not have uncertainty because you’re using your eyes, not “equipment.”

16. Table 1: Height of Wisconsin Fast Plant When Exposed to Blue Wavelengths of Light over 7 Days
#’d and descriptive Title
Complete column label with units
Date
Numbers of Days Passed
Height of Plant
(+/- 0.1cm)
Observations
9/6/12
1.3
3 leaves (all green); stem straight
9/7/12 1
1.4
3 leaves (2 all green & 1 has a small brown spot); stem straight
9/8/12 2
3 leaves (2 all green & 1 has a small brown spot); stem straight; 2 small gnats flying around
9/9/12 3
1.6
A 4th leaf has sprouted; gnats not visible today
9/10/12 4
1.7
4th leaf green and the 1 brown spot is bigger today (2 mm diameter)
9/13/12 7
2.1
2nd stem beginning to branch out; leaves are the same
Observations- detailed

17. Height of Plant (+/- 0.1cm)
Table 1: Trial #1- Height of Wisconsin Fast Plants When Exposed to Five Different Light Wavelengths over 7 Days
Height of Plant (+/- 0.1cm)
Date
Numbers of Days Passed
Blue Light
Green Light
Red Light
Yellow Light
White Light
Observations
9/6/12
1.3
1.0
#.#
B: 3 leaves (all green); stem straight G: R:
Y:-----
W:-----
9/7/12 1
1.4
B: 3 leaves (2 all green & 1 has a small brown spot); stem straight G:
9/8/12 2
B: 3 leaves (2 all green & 1 has a small brown spot); stem straight; 2 small gnats flying around
9/9/12 3
1.6
B: A 4th leaf has sprouted; gnats not visible today
G: Etc…
9/10/12 4
1.7
B: 4th leaf green and the 1 brown spot is bigger today (2 mm diameter)
9/13/12 7
2.1
B: 2nd stem beginning to branch out; leaves are the same
NOTE: how to label data when have 2 titles for a column
(height & color)
NOTE: data is all showing same # of decimal places
(“1.0” not “1”)
NOTE: observations for all colors each day

18. Practice scoring this table:
Mini-checklist: What is missing?
Title of Data Table
Columns & rows completely labeled
Observations
Level of equipment uncertainty
Level of precision (decimal places)

19. What is “processed” data?
This is the final data that you will use in order to answer your original research question.
If your question is looking to compare a rate, such as a growth rate:
Raw data: height (cm) for each unit of time (day)
Processed data  amount of growth in cm per day (cm/day)
You will use math (or a computer will use math) in order to convert your raw data into processed data.
An average is NOT considered enough to be counted as data processing (even though you will need to average trials before continuing into “processing”)

20. In order to process your data:
You need to consider what data you have & what you want the data to look like in order to answer your question.
If you are doing the math, you must show 1 example of each type of calculation
Should come between raw data and your presentation of your processed data (the table showing what you calculated)
Keep in mind you’re “telling a story”: 1) I collected data; 2) then I did this math; 3) which resulted in this final processed data
You must use all of your data points while processing. You don’t get to choose which data you like vs. what doesn’t fit what you want it to “say.”

21. Which processing is the weakest?
Background Raw data includes height of plant every school day totaling 10 data points over 12 days (plant still grows over the weekend)
trying to calculate rate of growth (cm/day)
(final height – initial height) /12 days
Graph raw data & take slope of the line
Calculate rate of growth between each recorded data point & then calculate the average of these
Height of Fast plant (cm)
Time (days)
Why is this the weakest?

22. Examples of scoring:
Background Raw data includes height of plant every school day totaling 10 data points over 12 days (plant still grows over the weekend)
Didn’t take weekends into account;
Slope (growth rate)= 0.21cm/day
This is a major mistake in processing!
Took weekends into account;
Slope (growth rate)= 0.16cm/day

23. Presentation= Table & Graph
When presenting your processed data in a table, it can be a new table or an extra column in an existing table.
Just like all tables, it needs to have a complete title, column headings, degree of precision (# decimal places), etc.
Also need to take into account  SIG FIGS
Don’t show your processed data to be more precise than the equipment you used to collect the data

24. Graphs are also numbered & have the same title as your table
Be sure you have the right type of graph
When labeling bar graphs (Excel calls them “column” graphs), take note of how to label the x-axis:
Complete label & unit below; ONLY numbers on x-axis line

25. What are the error bars based on?
-standard deviation!
-This is why you need at least 5 trials for each level of IDV
ºC should not be part of axis; it should only be underneath next to “temperature”

26. Analysis Scoring Practice: What is missing in this Raw Data Table?
Should have table #!
Temp listed under “maggot #”
Where are the observations??

27. Processing Data Scoring Practice: What do you see?
What if this student had only calculated an average?
An average is NOT sufficient math to be considered processing! Therefore, there isn’t any processing.

28. Presentation Scoring Practice: What do you see?
*Table # as part of title
*Equipment uncertainty
*Units should only be at top of column
*Missing the example/ sample calculation
Processed data should ALSO be in a table!
Units do not go on x-axis! They go with the label

29. Interpreting your Data: NOT the Evaluation/ Conclusion!
What trends or patterns are visible in the data?
Ex: positive correlation
Are there any results or groups of results that do not fit the overall trend or pattern?
Can anomalous results be explained by mistakes or are you unsure about the overall trend or pattern?
How much do the trials vary?
This indicates how reliable the evidence is.
Is there a statistical test you could do?
Link- Click me!

30. Bozeman Science: Standard deviation
Bozeman Science: Standard error
Bozeman Science: Statistics for science