Summative Productivity Lab Maxi Widjojo IB S1 ESS Mrs. .docx

Summative Productivity Lab Maxi Widjojo
IB S1 ESS
Mrs. Henard
Research Question (RQ): What is the effect of Temperature on
productivity at the Southern shore of
Lake Borodun?
Variables:
Variable(s):
Independent: Temperature of the water extracted from Lake
Borodun
Dependent: Oxygen concentration in the water from the lake
Raw Data:
Displays the data of initial amounts of oxygen concentration in
Lake Borodun in dark and light based on
the given temperatures.
Month: January March May June July September November

Temperature
(Celsius)
8 12 16 17 18 15 9
Initial amount
of dissolved
oxygen: (mg/l)
13.22 12.81 12.44 12.12 11.83 12.8 12.45
Trial 1: (Light) 14.65 17.00 17.21 18.08 17.91 17.12 16.23
Trial 2: (Light) 14.21 16.26 17.34 18.00 17.88 17.34 16.21
Trial 3: (Light) 14.44 16.55 17.68 17.86 17.77 17.15 16.11
Trial 4: (Light) 14.35 16.69 17.7 17.88 17.42 17.78 16.38
Trial 1: (Dark) 11.67 10.97 10.78 10.61 10.42 11.21 10.14
Trial 2: (Dark) 11.88 11.11 10.56 10.79 10.10 10.88 10.56
Trial 3: (Dark) 11.92 11.28 10.72 10.65 10.44 10.56 10.72
Trial 4: (Dark) 11.65 11.07 10.32 10.37 10.57 10.38 10.32
Processed Data Table: Displays the relationship between the
amount of productivity and the given
temperatures.
Sample average calculation for 12℃℃ = (12℃℃ for light):

17.00 + 16.26 + 16.55 + 16.69 = 66.5
= 66.5 / 4 = 16.63
Month: January March May June July September November
Temperature
(Celsius)
8 12 16 17 18 15 9
Initial amount
of dissolved
oxygen: (mg/l)
13.22 12.81 12.44 12.12 11.83 12.80 12.45
Trial 1: (Light) 14.65 17.00 17.21 18.08 17.91 17.12 16.23
Trial 2: (Light) 14.21 16.26 17.34 18.00 17.88 17.34 16.21
Trial 3: (Light) 14.44 16.55 17.68 17.86 17.77 17.15 16.11
Trial 4: (Light) 14.35 16.69 17.78 17.88 17.42 17.78 16.38
Average
(Light): mg/l
14.41 16.63 17.53 18.00 17.75 17.35 16.23
Trial 1: (Dark) 11.67 10.97 10.78 10.61 10.42 11.21 10.14
Trial 2: (Dark) 11.88 11.11 10.56 10.79 10.10 10.88 10.56

Trial 3: (Dark) 11.92 11.28 10.72 10.65 10.44 10.56 10.72
Trial 4: (Dark) 11.65 11.07 10.32 10.37 10.57 10.38 10.32
Average
(Dark): mg/l
11.78 11.12 10.60 10.61 10.38 10.76 10.44
Processed data table for the relationship between temperature
and productivity:
Sample calculations for data table below:
NPP: Light - Initial E.g. for 8℃℃: 14.41 - 13.22 = 1.19
R: Dark - Initial E.g. for 8℃℃:
GPP: NPP + R E.g. for 8℃℃:
Temperature (℃) NPP (Net Primary
Production)
R (Respiration) GPP (Gross
Primary
Production)
8 1.19 -1.44 -0.25

12 3.82 -1.69 2.13
16 5.09 -1.84 3.25
17 5.88 -1.51 4.37
18 5.92 -1.45 4.47
15 4.55 -2.04 2.51
9 3.78 -2.01 1.77
Calculations for processed data table for average oxygen
concentrations according to varying
temperatures: (mg/)
Sample average calculation for 12℃℃ = (12℃℃ for light):
17.00 + 16.26 + 16.55 + 16.69 = 66.5
= 66.5 / 4 = 16.63
Light:
1. (8℃ for light): 14.65 + 14.21 + 14.44 + 14.35 = 57.65
= 57.65 / 4 = 14.41
2. (12℃ for light): 17.00 + 16.26 + 16.55 + 16.69 = 66.5
= 66.5 / 4 = 16.63
3. (16℃ for light): 17.21 + 17.34 + 17.68 + 17.78 = 70.13

= 70.13 / 4 = 17.53
4. (17℃ for light): 18.08 + 18.00 + 17.86 + 17.88 = 71.82
= 71.82 / 4 = 18.00
5. (18℃ for light): 17.91 + 17.88 + 17.77 + 17.42 = 70.98
= 70.98 / 4 = 17.75
6. (15℃ for light): 17.12 + 17.34 + 17.15 + 17.78 = 69.39
= 69.39 / 4 = 17.35
7. (9℃ for light): 16.23 + 16.21 + 16.11 + 16.38 = 64.93
= 64.93 / 4 = 16.23
Dark:
1. (8℃ for dark): 11.67 + 11.88 + 11.92 + 11.65 = 47.12
= 47.12 / 4 = 11.78
2. (12℃ for dark): 10.97 + 11.11 + 11.28 + 11.07 = 44.43
= 44.43 / 4 = 11.12
3. (16℃ for dark): 10.78 + 10.56 + 10.72 + 10.32 = 42.38
= 42.38 / 4 = 10.60

4. (17℃ for dark): 10.61 + 10.79 + 10.65 + 10.37 = 42.42
= 42.42 / 4 = 10.61
5. (18℃ for dark): 10.42 + 10.10 + 10.44 + 10.57 = 41.53
= 41.53 / 4 = 10.38
6. (15℃ for dark): 11.21 + 10.88 + 10.56 + 10.38 = 43.03
= 43.03 / 4 = 10.76
7. (9℃ for dark): 10.14 + 10.56 + 10.72 + 10.32 = 41.47
= 41.47 / 4 = 10.44
Calculations for the NPP, R, and GPP:
Sample calculations
NPP: Light - Initial E.g. for (8℃℃): 14.41 - 13.22 = 1.19
NPP (Net Primary Production) Calculations:
1. (8℃): 14.41 - 13.22 = 1.19
2. (12℃): 16.63 - 12.81 = 3.82
3. (16℃): 17.53 - 12.44 = 5.09

4. (17℃): 18.00 - 12.12 = 5.88
5. (18℃): 17.75 - 11.83 = 5.92
6. (15℃): 17.35 - 12.80 = 4.55
7. (9℃): 16.23 - 12.45 = 3.78
R (Respiration) calculations:
R: Dark - Initial E.g. for (8℃℃): 11.78 - 13.22 = -1.44
1. (8℃): 11.78 - 13.22 = -1.44
2. (12℃): 11.12 - 12.81 = -1.69
3. (16℃): 10.60 - 12.44 = -1.84
4. (17℃): 10.61 - 12.12 = -1.51
5. (18℃): 10.38 - 11.83 = -1.45

6. (15℃): 10.76 - 12.80 = -2.04
7. (9℃): 10.44 - 12.45 = -2.01
GPP (Gross Primary Production) calculations:
GPP: NPP + R E.g. for (8℃℃): 1.19 + (-1.44) = -0.25
1. (8℃): 1.19 + (-1.44) = -0.25
2. (12℃): 3.82 + (-1.69) = 2.13
3. (16℃): 5.09 + (-1.84) = 3.25
4. (17℃): 5.88 + (-1.51) = 4.37
5. (18℃): 5.92 + (-1.45) = 4.47
6. (15℃): 4.55 + (-2.04) = 2.51
7. (9℃): 3.78 + (-2.01) = 1.77

Graph: Temperature vs amount of productivity
Data analysis:
Based on the graph and processed data above, it is evident that
there is a positive correlation
towards the amount of productivity and the temperature in the
Southern Shore of the Lake Borodun. The
productivity found based on the given temperatures was through
the use of the formulas of GPP, NPP
and R. This allowed the calculations for the processed data,
however the data used for the graph was
only using the calculations for the net primary productivity
(NPP). Based on the numbers, the productivity
throughout the experiment ranges from around 1-6. Looking at
the data we can infer that as the
temperature rises so does the amount of productivity. This is a
positive relationship between the
temperature and the productivity, as seen on the graph, the x-
values go up, which in this case is the
temperature, so does the y-value, which is the productivity from
the lake. For example at (8℃) the NPP is

1.19, while at (18℃) the NPP is 5.92, which is why it is
understood that there is more productivity found in
higher temperatures. The reasoning behind the increase in the
productivity as the temperature gets
higher is due to the fact that the plants in the lake would be
exposed to more sunlight, thus allowing them
to photosynthesize, creating a larger amount of dissolved
oxygen (DO2) in the lake. On the other hand,
as the temperatures get colder, the plants in the lake are
exposed to a significantly lower amount of
sunlight than higher temperature weather, which limits the
amount of sunlight exposure the plants in the
lake receive, thus lowering the plants ability to photosynthesize.
This brings us to the conclusion that the
increasing temperatures throughout the this experiment have
been evident to display that they show more
productivity than lower temperatures.
Conclusion:
Declining fish stocks have been an arising environmental issue
that has been affecting those from
lower-class societies as they have a very limited source of
protein due to the shortage of fish. With the
improved fishing technology, and increased number of fishing
boats, the corporations behind the
operations have claimed a very significant amount of the fish,
depriving many people with less fortunate
backgrounds by limiting their access to a proper nutrition. This
has has had a major impact on people with
impoverished backgrounds as they now have a shortage on a
staple source of protein, as many lakes and
oceans have been exploited by fishing boats for it’s resources.

The data collected above, has been
evident to show the increase in temperature and how it has been
highly beneficial towards the productivity
of the fish. Relating back to the research question: What is the
effect of Temperature on productivity at
the Southern shore of Lake Borodun? From the data we can
conclude that there is indeed an impact on
the productivity of the fish based on different temperatures, as
the data has been evident to show that
there is a positive correlation between the increasing
temperatures and the amount of productivity in the
lake. The data has shown that the productivity is highest in
July, as the weather holds it’s highest
temperature then, which will result in the fish being highly
populated in the area due to the algae
productivity being high as well, making July the month best for
fishing. Overall the data collected was
accurate, as there was no outliers due to human error in the lab.
However, the data did hold two outliers,
but that was due to the low temperatures. Furthermore, this lab
has brought us to answer the research
question, as the data collected has proven the relationship
between temperatures and productivity in the
lake.
Evaluation:
Strengths: Weaknesses: Opportunities for further
studies / limitations:
Sufficient data is a strength in
this lab, as it has created a clear
perspective to view the

relationship between
temperature and productivity to
see whether or not the trend
was consistent throughout the
trials.
The data collected was only
from the first days of the month,
which is a weakness because
there would be no information
on the activity of the productivity
for the rest of the month,
causing the data to possibly
show inaccuracy when stating
the productivity throughout the
months.
This would be a limitation on the
research of declining fish stocks,
as it would not provide
information on the productivity
throughout the month, but only
on one day of the month, which
would possibly create inaccurate
data on the most productive
months for the fish. This could
be an opportunity for further
study, as water samples could
be taken once a week and then
averaged at the end of the
month to be more accurate. This
would allow more information on
the productivity of the lake, to
give a better idea on what the
productivity would look like
throughout the month, as

opposed to just taking data from
one day of the month and
presuming it remains consistent
for rest of the month.
The lab was conducted using
high technology items, which is
a strength because it ensured
that the data was accurate and
allowed minimal risk for
systematic errors to be
conducted throughout the data
collection.
The lab displays data from 7
months out of a year, and leaves
out 5 months, which could
possibly resort to inaccuracy in
calculating the most productive
month, as there is no
information on the temperature
nor productivity in the left out
months.
This would be a limitation as
there would be insufficient data
to display the most productive
month, as the data collected had
left out 5 months in the year.
This would make the data not as
accurate as it is only collecting
data from 7 months, and there
would be no information on the

temperature and productivity for
the other 5 months. However
this is also an opportunity for
further study, as data could be
collected for all 12 months to get
a better understanding on the
amount of productivity there is
throughout the whole year. It
would also allow a more clear
perspective on which month is
more productive, as there would
be data on every month, as
opposed to only 7 months.
Solution
to the environmental problem of declining fish stocks:
The data above suggests a relatively accurate solution towards
the problem of declining fish
stocks, as it provides information on which months are most
productive and the best for fishing. The data
provides sufficient information to see the trend line on the
relationship between temperature and net

primary productivity (NPP), however it holds some limitations
on the accuracy of the most productive
month.
The data displayed only shows 7 months in the year which
leaves us oblivious towards the
information on the productivity and temperatures for the
remaining 5 months. This is would impact the
data as the months left out would have had a positive effect on
the data by showing more information. It
also could possibly have displayed a more productive month
than July, which is the most productive
month out of the 7 months the data was collected in. Another
limitation on using this current method
would be the fact that the data was only collected on the first
day of the month, which leaves a lack of
information on the productivity of the rest of the month, as the
productivity is likely to fluctuate and not
remain consistent due to constant temperature changes.
Despite the current limitations in the current method, with
improvements made, this could most

definitely be an option to be considered as a solution to the
environmental problem of declining fish
stocks. If the method were to be slightly altered in ways such as
taking data from every week of the month
and then averaging it out, as opposed to taking data one day of
the month and presuming it remains
consistent throughout the rest of the month. By improving this
part of the method, it would allow more
accuracy to be ensured as the productivity levels for each month
would be more precise, due to the fact
that there would be more information on the fluctuation of the
productivity. Additionally, including the
months that were left out, would also give more insight on the
productivity levels throughout the year,
which would be more beneficial for analyzing the most
productive months, as there would be a lot more
information to consider.
Furthermore, by changing the method slightly to fulfill the
opportunities for further study, this

method would be very beneficial towards analyzing the most
productive months as it would be a very
informative source for analyzing the months individually to see
the peak for fishing times. With the use of
the data collected above it would give insight on the months
which are economical to fish in, allowing
fishermen to still benefit despite the environmental problem of
declining fish stocks. By using this method
after going through several alterations, this could be highly
beneficial to use for fishing, as it provides
information on productivity and the relationship it holds with
temperature, which could be useful as well,
as they could also go fishing based on the daily temperatures.

ESS LAB REPORT GUIDE
IDENTIFYING THE CONTEXT AND THE RESEARCH
QUESTION
A good format is “How does [the independent variable] affects
[the dependent variable] in [the context of your experiment]?”
e.g. How does hair length affects the insulating power of fur in
polar bears (Ursus maritimus)?
e.g. How does salinity affect the germination of wheat seeds?
You need to state a focused research question. To do this you
should:
a) Briefly state what you are trying to find out.
b) Include both the independent and dependent variables.

Format the RQ according to the box on the right.
c) State the context of your investigation. You need to discuss a
local or global environmental context/issue and explain the
links between your research question and the issue.
PLANNING
a) The method needs to be repeatable, so write it as though
someone else could do your lab with your instructions.
b) You must justify your choice of method, which is often a
sampling method.
c) You must consider the quantity of data needed to be
sufficient (enough) to answer the research question.
d) You must plan to make your readings as precise and accurate
as possible through the use of appropriate equipment.
Variables are factors that may affect the outcome of your
experiment. They are measurable factors, not pieces of
equipment. Do not use the word “Amount”. It is not specific
enough – terms like mass or volume are better.

Independent variable: This is the variable that you manipulate –
you choose the values to investigate.
Dependent variable: This is the variable that changes in
response to changes in the independent variable. It is what you
are measuring or trying to find out.
Controlled variables: These are other factors that may also
affect the dependent variable. They need to be kept constant in
order to ensure a fair test.
PLANNING: Identify the variables
Example
Controlled variable
Method to control the variable
Temperature at which reaction occurs
The test tubes in which the reaction occurs will be placed in a

water bath set to 40C for the duration of the reaction.
Duration (time) of the reaction
The reaction will be allowed to proceed for 300 seconds. This
will be timed using a stopwatch (0.1seconds).
a) State the independent variable.
b) State the range or extent of the independent variable that will
be tested e.g. 1g, 5g, 10g etc
c) State the dependent variable.
d) State how the dependent variable will be measured (if it can’t
be measured directly). E.g. You may measure time and volume
then calculate rate.
e) List several controlled variables. Choose the ones which
might have a real effect on the dependent variable, not less
important ones that may not.
You should explain in the method you will use to control each
of the (controlled) variables.
f) Give brief (but specific) explanations of how you will control
(keep at a constant value) each variable.
g) If a variable cannot be controlled, state this. Then describe
how you will try to minimize any change and/or how you will
monitor the variable.

PLANNING List the equipment (apparatus and materials)
needed
Choose appropriate equipment.
Make sure that your equipment list includes all of the
following:
a) All of the equipment and materials needed for the experiment
(after writing you method read through it and check of the items
used as you go on your equipment list)
b) Numbers of items (e.g. 2 scalpels)
c) Volumes and concentrations of any solutions needed (e.g.
300ml of 0.5M hydrochloric acid)
d) Precision (and range if appropriate) of all measuring
instruments.
e) Sizes of beakers or other items (e.g. 250ml beaker, 10cm
length of dialysis tubing)
f) If you can, put the uncertainty of the accuracy of each piece
of equipment, (e.g. 50ml measuring cyclinder, ±0.5ml.)

PLANNING Write a method
You need to collect sufficient relevant data. This means there
should be enough data over a wide enough range to adequately
answer the research question.
a) What values of the independent variable should you test?
i. How many values should you test? Decide how many values
will be needed to show any trend or pattern. Always plan for an
ideal situation – worry about time constraints later.
Number of Values
If you are looking for a correlation, usually (not always) 5
different values are needed but the more the better. Sometimes
less are appropriate depending on context.

ii. What is an appropriate range of values?
Range of Values
You may need to do some research to help you decide.
e.g. if testing productivity in aquatic plants, then a range which
represents the natural extremes normally experienced by the
plants would be sensible, e.g. 10, 13, 16, 19, 22 degrees etc
b) How will you measure your independent and dependent
variables?
a. Can you measure it directly (raw data) or do you need
measure other values and use them to calculate values
(processed data) for your independent variable?
b. What measuring instruments will be best to use? Do you
know how to use them?
c. What level of precision is required in your measurements?
d. What units will you use to record your measurements?

c) How may trials or replicates need to be carried out?
Number of Trials
Environmental systems, because of their complexity and normal
variability, usually require replicate (repeated) observations and
multiple samples of material. A good rule is to aim for 5, but
sometimes this is not necessary. For example, if you measure
turbidity or water temperature and get the same reading twice, it
is unlikely to be necessary to do more readings.
A clear, easy to follow method is necessary for good
communication.

Imagine that someone (who has not done the experiment before)
should be able to follow your procedure and get similar results.
The following features contribute to writing a good method.
a) The method can be written as instructions like a recipe
b) Do not begin with “Gather all of the materials” … it is kind
of a given that you will do this!!
c) Use numbered steps (rather than paragraphs).
d) Use a diagram if possible to show how to set up any
equipment. Then you can say “Set up the equipment as shown in
the diagram”. This would save you writing a lot of words.
e) Specify what will be measured (and the units to be used)
f) Include details of how you will measure values
RESULTS, ANALYSIS AND CONCLUSION
RAW DATA refers to the values from the measuring
instruments exactly as they were shown. Once you do any
addition, subtraction, multiplication or division then it becomes
PROCESSED DATA.

1. Record ALL your raw data
Quantitative data – numerical values obtained from the
measuring instruments (e.g. temperature, mass etc) or by other
means e.g. counting
Qualitative data – non-numerical observations. Other
observations made during your experiment that may have a
bearing on the conclusion or help to explain patterns and trends
(or the lack of!). Examples include changes in colour, texture,
size etc. Any other observed sources of error should also be
recorded.
Even if the data is unexpected or contains mistakes, you must
show it.
Raw data should include quantitative (always!) and qualitative
(almost always) data.
Raw data should be displayed in a table (qualitative data may
require some other format, but a table is till usually best).

a) Formatting your table
a. When possible, the independent variable should come first in
your columns followed by the dependent variable.
b. Show lines around all rows and columns
c. Make it clear. A good table should be able to be understood
out of context (i.e. you can understand it without the rest of the
lab report)
b) Title
a. Title should describe the data contained in the table. It should
include the key variables as well as any specific conditions of
the experiment
b. If there is more than one table, number them.

EXAMPLE
Table 1: The relationship between temperature and water uptake
in a leafy shoot of a geranium (Geranium carolinianum)
c) Headers and units
a. Columns should be clearly annotated with a header, and units
(in the heading not with the data)
b. Headings should indicate what the data is in the column
below
c. Headings are likely to be the name of a variable (independent
or dependent)
d) Precision of data
There is no variation in the precision of raw data; the same
number of decimal places (significant figures) should be used.
e) Anomalous results
Any results that are particularly different from the others need
to be identified and excluded from any processing (but shown in

the raw data).
Here are some examples of decent tables. Yours might be very
different.
Here is a table of qualitative data:

2. Process your data
Data processing involves combining and manipulating raw data
to determine the value of a physical quantity (adding,
subtracting, squaring, dividing), and taking the average of
several measurements and transforming the data into a form
suitable for the graphical representation.
a)Look at Your Research Question
ALWAYS CONSIDER YOUR RQ.
The purpose of processing data is to show patterns in the data
that help you draw a conclusion that answers your research
question
b)Choose Your Processing Technique, some options are

Change in quantities
· Change in quantities (initial final)
This is a very basic processing technique and should be used in
combination with other methods.
Percentage change in quantities
· Percentage change in quantities.
This also allows you to compare quantities that have
different initial and final quantities
· Rate
Rate

Rate is a measure of how quickly a variable changes
· Mean (average)
When you have multiple trials in an experiment, calculate the
mean.
3. Present your processed data
You are expected to decide upon a suitable presentation format
without teacher assistance.
a)Present data so that stages of calculation can be followed
Show one fully worked sample calculation for each type used.
If Excel or a graphing calculator was used to generate values
simply state this.
b)Decimal places
Your processed data should not have more decimal places (or
significant figures) than the raw data you collected
c)Presentation formats
A few general options are listed below; but you are not limited
to these options
· Spreadsheets and tables showing data calculations such as
mean, percentage change etc.

· Line graphs and scatter-plots showing continuous data points
(e.g. time, concentration, age, heart rate, height etc.) with line
of best fit
· Bar graphs showing discrete data (categories) e.g. species,
phenotype, sex, ethnicity
· Pie charts showing percentages out of 100%
· Biological diagrams to illustrate changes in appearance
(should be used in combination with other methods)
· Photographs
Diagrams and Tables
There should be clear, unambiguous headings for diagrams and
tables or graphs similar to the headings used for tables in your
data collection.
Diagrams will be labelled as figures. Figures should be
numbered for reference and be placed below the figure it
references.
Graphs
A graph is a visual representation of the data that allows you to
answer the research question. It should look like this;

Dependent variable/ units
Independent variable/ units
Graphs must have the following:
· Title. The same expectations apply as for table (see section on
Recording Raw Data).
· Appropriate scales; if you are measuring temperatures between
30 and 40 degrees, your graph should not begin and end at 0 and
100 respectively. Your units must be appropriate as well. If
you are measuring in mm, you shouldn’t have meters marked on
your graph.

· Labeled axes with units; axes should be labeled similarly to
your table headings.
· Accurately plotted data points should be clearly shown,
visible, and not too big as to obscure data
· A suitable best-fit line, trend line or curve is drawn (for a line
graph or scatter plot) DO NOT CONNECT THE DOTS !
Do not let the software choose the formatting or the best fit
line. The best fit line should be chosen by you to reflect the
trend that you judge to be appropriate.
You may choose to add standard deviation or range of data error
bars and R2. The error bars should be labelled below the graph
e.g. “Error bars show ±1 STDEV.” If you include error bars or
R2, you must include an analysis of what they tell you.
Here are some decent graphs. Yours may be very different.

4. Write a conclusion
· Clearly state any patterns or trends you see in the data.
· Be very careful to answer your research question.
· Do not miss smaller patterns. For example, even though the
trend might be “downwards” there may be a plateau at the start,
or the end.
· Use your results to justify your conclusion, state actual
figures.
· Describe what your results show in the context of your topic
of investigation
· Compare these to literature, scientific understanding or models
or class discussion. If there are differences, identify them and
suggest possible reasons

· Identify any anomalous results and justify their exclusion from
processing
· All sources used to write your lab report should be fully
referenced by using MLA formatting.
Don’t lie! State what you see, not what you wished to see.

DISCUSSION AND EVALUATION
1. Evaluate your conclusion in context
Does your conclusion fit in with the context? Does it support, or
not support the ideas of the context. Don’t just make simple
statements. consider ways in your conclusion does or does not
fit in with the context.
“Evaluate” means make judgments about the extent to which
your data fits in with the ideas you have learned. How valid are
your own conclusions? How well have you answered the
research question?

Mention R2 or error bars to justify the validity of your
conclusions.
2. Discuss strengths and weaknesses in your investigation
This is where you comment on the design, method of the
investigation, and the quality of the data. A good format for the
Evaluation is shown to the right.
Good format for Evaluation
Strength/Weakness
Significance
Improvement
a) List specific strengths in the design and carrying out of the
procedure.

b) List specific weaknesses in the design and carrying of out the
procedure.
For both of the above consider….
i. procedures,
ii. limitations and use of equipment,
iii. management of time, investigation timing
iv. data quality (sufficiency, accuracy and precision) and
relevance of data.
v. Unavoidable errors, such as variability of materials
c) For each strength or weakness discuss its significance i.e. it’s
effect on your results e.g. values too high/low, data values less
reliable (large uncertainty/error/S.D. would indicate this),
measurements less accurate or precise, trend/pattern incorrect or
unclear etc
Acceptable Example:
“Because the simple calorimeter we used was made from a tin
can, some heat was lost to the surroundings—metals conduct
heat well. Therefore, the value we obtained for the heat gained
by the water in the calorimeter was lower than it should have
been. The heat lost from the tin can would not have been a lot in
the time taken for the experiment so this probably did not have

a significant impact on the results”
Unacceptable Examples:
"The test tubes weren’t clean.” careless or poor performance
does not make for a valid weakness
“Human error.” a specific description of the type of human
error would be required
Describe improvements for each identified weakness
For each improvement ensure that:
a) Suggestions are specific (numerical if possible). “Next time
we should work more carefully” is not acceptable.
b) Suggestions are realistic – they can be achieved within the
constraints of the timetable, school setting and budget.

c) Improvements are not overly simplistic or superficial – you
need to demonstrate that you are a student at a Diploma level!
Accuracy = how close a measurement is to the correct value
Precision = exactness of a measurement as represented by the
number of decimal places to which it is expressed
Reliability = consistency in measurements (i.e. if measurements
taken over consecutive trials are all very similar then there is
consistency and they are said t be reliable). This can be shown
by the standard deviation.
APPLICATIONS

Based on what you have learnt in your experiment, either
· Apply this knowledge to the environmental context/issue
under investigation, or
· Suggest a solution to the environmental issue.
You should justify your application or solution by directly
linking your work with the context/issue, and providing
evidence from your findings to support your application or
solution.
Evaluate your application or solution. Again, this means
judging your own ideas – give pros and cons of your idea.
COMMUNICATION
To score well on communication:
· Answer all sections in order.
· Use headings and titles throughout.
· Use suitable language (no slang, don’t “grab” your
equipment.)
· Use the specific terms you have used in the course.
· Use a sensible font (12pt) and avoid the use of colour in tables
(only use it if necessary in graphs)
· Avoid cramping too much onto a page.

· Follow all conventions regarding tables, graphs and other
figures.
· Cite any and all sources.
9
ESS Lab Report Guide
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Generalised lab scaffold
CRITERION 1 Identifying the context
Aspect 1 – States a relevant, coherent and focused research
question

How does the IV affect the DV, then add “focusing factors”
such as where, when, what, who and how measured?
Aspect 2 - Discusses a relevant environmental issue (either
local or global) that provides the context for the research
question.
What parts of the syllabus relate to this environmental issue?
Aspect 3 – Explains the connections between the environmental
issue and the research question
Suggest a hypothesis. In this way, the IV and DV are related to
one another and the rationale behind the relationship can
explain the connection of the RQ to the issue.
Why is it important or relevant that we bother to do this? Why
does it matter?
CRITERION 2 – Planning
Aspect 1 – Designs a repeatable method appropriate to the
research question that allows for the collection of sufficient
relevant data
Use bullets or numbers, quantify, add equipment to take ALL

measurements, add sufficient range of values for IV and always
have trials for sufficient data.
Aspect 2 – Justifies the choice of sampling strategy used
Justify relevant steps as you write your plan.
Aspect 3 – Describes the risk assessment and ethical
considerations where applicable
Not just lab safety, safety working outdoors, possible sensitive
data on pollution, making surveys anonymous etc.
CRITERION 3 – Results, analysis and conclusion
Aspect 1 – Construct diagrams, charts and graphs of all the data
in an appropriate way
All RAW data shown, graph as suitable, must have IV vs DV
Aspect 2 – Analyse your graph, stating all the patterns and
trends
Aspect 3 – Interpret the patterns and trends to lead you to a
conclusion
· Write a conclusion which answers your research question, and

outline why you have reached that conclusion by quoting data
CRITERION 4 – Discussion and evaluation
Aspect 1 – Evaluate your conclusion
· Are you confident it is correct, does it make sense when you
look at the background information
Aspect 2 – Strengths, weaknesses and limitations of the method
· Did you find enough data?
· What other data would you have liked?
· How reliable were your websites?
Aspect 3 – How could you make your method better?
· What data would you need?
· Would you expand to other years / communities?

CRITERION 5 – Application
Aspect 1 – Justify an application
· How could governments or individuals use this data?
· Would it help the environmental issue or problem?
Aspect 2 – Evaluate your suggested application
· Would your suggested way of applying the data be practical or
do-able?

Summative Productivity Lab Maxi Widjojo IB S1 ESS Mrs. .docx

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