Errors and Uncertainties in Biology Internal
Assessment
The treatment of errors and uncertainties is relevant in the internal assessment criteria of:
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data collection, aspect 1 (collecting and recording raw data)
data processing and presentation, aspect 2 (presenting processed data)
conclusion and evaluation, aspects 1 and 2 (drawing conclusions/evaluating procedure(s)
and results).
Biology is not an exact science. Much of the data collected by biologists is qualitative, and
biological systems are complex and difficult to control. Biological investigations, nevertheless,
often require measurements, and biology candidates need to be aware of the sources of error in
their data, qualitative or quantitative. There are many similarities to environmental systems, and
teachers should also refer to the section Errors and Uncertainties in Environmental Systems
Internal Assessment.
An appreciation of error should be apparent at all stages of a report on an investigation.
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In the planning stages where the limitations of the time and the materials should be
assessed, and the potential sources of error should be controlled.
In the data collection, and processing and presentation stages where the degree of
accuracy of a measuring device should be stated.
In the evaluation of the investigation where the sources of error should be discussed,
along with the possible ways of avoiding them.
Although candidates should analyse their investigations for sources of error, they should not be
led to conclude that, with all such sources of error and imprecision, experimental results are
worthless. Experimental results are only estimates.
Scientists are expected to:
1. make every effort to avoid errors in their design of investigations and in their use of
instruments
2. be aware of the sources of error and appreciate their magnitude.
Examples of Errors
Human Errors
Human errors can occur when tools or instruments are used or read incorrectly. For example, a
temperature reading from a thermometer in a liquid should be taken after stirring the liquid and
with the bulb of the thermometer still in the liquid. Thermometers and other instruments should
be read with the eye level with the liquid to prevent parallax error. Human errors can be
systematic, because the experimenter does not know how to use the apparatus properly, or they
can be random, because the power of concentration of the experimenter is fading.
Systematic Errors
Systematic errors can be reduced if equipment is regularly checked or calibrated to ensure that it
is functioning correctly. For example, a thermometer should be placed in an electronic water bath
to check that the thermostat is correctly adjusted, and a blank should be used in a colorimeter to
compensate for the drift of the instrument.
Random Errors
In biological investigations, errors can be caused by changes in the material used, or changes in
the conditions under which the experiment is carried out. Biological material is notably variable.
For example, the water potential of potato tissue may be calculated by soaking pieces of tissue in
a range of concentrations of sucrose solutions. However, the pieces of tissue will vary in their
water potential, especially if they have been taken from different potatoes. Random errors can be
kept to a minimum by careful selection of material and careful control of variables. For example,
using a water bath or a blank.
Human errors can become random when people have to make a large number of tedious
measurements and, therefore, their concentration spans vary. Automated measuring, using a data
logger system, can help reduce the likelihood of this type of error. Alternatively, the
experimenter can take a break occasionally.
Replicates and Samples
Biological systems, because of their complexity and variability, require replicate observations
and multiple samples of material. As a rule, the lower limit is five measurements, or a sample
size of five. Very small samples run from 5 to 20, small samples run from 20 to 30, and big
samples run from 30 upwards. Obviously, this will vary within the limits of the time available for
an investigation. Some simple investigations permitting a large sample, or a large number of
replicate measurements, could be included in the scheme of work to reinforce this point. It is also
possible to use class data to generate sufficient replicates to permit adequate processing of the
data.
Candidates should be made aware that if a reading is particularly different from the others, it
may be left out of the processing and analysis. However, candidates must always justify why
they have chosen to do this.
Degrees of Precision
The accepted rule is that the degree of precision is ± the smallest division on the instrument. For
example, a leaf measured using a ruler whose smallest division is 1 mm would measure 4.5 cm ±
0.1 cm. Candidates must choose an appropriate instrument for measuring such things as length,
volume, pH and light intensity. This does not mean that every piece of equipment needs to be
justified, and it can be appreciated that, in a normal science laboratory, the most appropriate
instrument may not be available.
The Act of Measuring
When a measurement is taken, this can affect the environment of the experiment. For example,
when a cold thermometer is put in a test tube of warm water, the water will be cooled by the
presence of the thermometer, or when the behaviour of animals is being recorded, the presence
of the experimenter may influence the animals’ behaviour.
Manipulative Skills and Personal Skills in
Biology Internal Assessment
Assessing Manipulative Skills and Personal Skills (a) and (b)
1. Investigating How Animals Adapt to Different Parts of a
River
Candidates investigate how different animals adapt to two areas of a river: one where the water is
fast moving (lotic) and the other where the water is still/slow moving (lentic). Candidates are
given two hours to plan the investigation and three hours to carry out the study. Access to the
appropriate field equipment is provided.
Manipulative Skills
Click here for a description of the manipulative skills criterion.
Carrying out techniques safely
Working in the field, especially in an aquatic environment, presents a number of safety issues to
which candidates must adhere.
Following a variery of instructions
This investigation is ideal for the assessment of manipulative skills, even if it is designed by the
candidates themselves. Each activity is repeated several times (measuring current speed,
sampling organisms). Situations in the field are sufficiently variable for candidates to find
themselves in new circumstances.
Personal Skills (a)
Click here for a description of the personal skills (a) criterion.
This depends upon how the investigation is arranged. Fieldwork often requires assistance so
working in a group, with each member having a definite role, requires collaboration.
Personal Skills (b)
Click here for a description of the personal skills (b) criterion.
Approaching scientific investigations with self-motivation and perseverance
It is difficult to assess this aspect if candidates are working in groups. However, if the
investigation is sufficiently complex to require a distribution of roles, the perseverance of the
individual can be assessed in group work. Working under field conditions, where the weather
may not be clement, may also reveal a temptation to rush through results.
Working in an ethical manner
Sampling and handling living organisms provides an opportunity to test the candidates’ attitudes
towards biological materials. Research on the characteristics of these organisms allows
candidates to demonstrate their attitudes towards the authenticity of data. Again, working under
field conditions, where the weather may not be clement, may reveal a temptation to invent results
in order to finish more quickly.
Paying attention to environmental impact
Fieldwork of this sort can be used to test the candidates’ attitudes towards the environmental
impact of the investigation (consideration of numbers of samples, distribution of samples and
type of sampling). Some methods can be very destructive. Restoration of the fauna and the
environment (eg plants and rocks) after the investigation can indicate attitudes.
2. Investigating the Effect of Light Intensity on
Photosynthesis
Candidates design an experiment to investigate the influence of light on photosynthesis. Each
candidate is given a sample of Elodea and access to the appropriate laboratory equipment.
Manipulative Skills
Carrying out techniques safely
If candidates are using more complex apparatus (for example, apparatus that would capture the
bubbles and permit their volume to be measured), manual dexterity will be required in setting it
up and using it successfully. The use of the lamp, and its potential proximity to water, requires
attention to safety.
Following a variety of instructions
If candidates choose complex apparatus, then it will be necessary to follow instructions on how
to set it up and use it.
Personal Skills (b)
Approaching scientific investigations with self-motivation and perseverance
A considerable amount of patience is required for this investigation, and self-motivation and
perseverance will be needed to see it through to completion.
Working in an ethical manner
Candidates can lose patience and be tempted to invent values to support their hypotheses.
Authenticity of data can be evaluated here. Care and respect for the delicate apparatus can also
be observed.
Paying attention to environmental impact
Although this experiment is carried out in the laboratory, candidates will show if they have a
responsible approach to working with living material, and if they are aware of the potential
impact of being careless with it.
3. Investigating the Effect of an Environmental Stimulus on
the Behaviour of the Common Cricket
Candidates design an experiment to investigate the effect of an environmental stimulus on the
behaviour of the common cricket. Two empty 50-litre glass fish tanks and 50 common brown
crickets are provided. Additional laboratory equipment can be requested.
Personal Skills (b)
Approaching scientific investigations with self-motivation and perseverance
This investigation continues for several days and involves considerable self-motivation and
perseverance in order to take all the planned readings.
Working in an ethical manner
Candidates may be tempted to fabricate data rather than make the effort to take readings twice a
day. Their approach to both living and non-living materials can be observed, particularly in their
care of the crickets and in their attitude towards keeping them in the light or in the dark for five
days.
Paying attention to environmental impact
If candidates provide their own living material, their awareness of the environmental impact can
be gauged by their behaviour while catching the organisms, and also in whether they return them
safely to their original habitat.
Planning (a) and (b) in Biology Internal
Assessment
Planning a scientific investigation is not done in a vacuum. For productive plans to be developed
candidates will need to have:
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acquired some theoretical background
made observations in the relevant field
experience in certain manipulative skills specific to the investigation
an understanding of the scientific method (the importance of hypothesis testing, replicates,
independent and dependent variables, controls, sampling techniques).
Candidates will need to have their limits set, and counselling will be necessary in feasibility
(time and cost), safety and ethics. For the sake of assessment, the candidates should plan their
investigations individually. However, it is possible that an initial brainstorming exercise would
be useful to generate ideas that could then be further developed by individuals. It is also possible
for candidates to work in groups on their planned investigations after their individual plans have
been assessed.
Investigations Suitable for Planning (a) and (b)
Click here for a description of the planning criteria.
Candidates would be given only the titles of the following investigations. The titles are general to
give candidates a sense of direction, but they are not sufficiently specific to serve as a research
question. Candidates will have to modify the title to make it more specific and focused. In most
cases, this will involve indicating the variable(s) chosen for investigation.
Abiotic Factors and their Effects
1. Investigate the effect of an abiotic factor on the distribution of an organism on a rocky
shore/a stream
Prelab/skills: a preliminary visit to make observations, identification of animals or plants,
measurement of variables, sampling techniques.
Possible variables: distance above low watermark, density of other organisms, exposure to sea,
current speed, light intensity, temperature.
2. Investigate the behavioural responses of woodlice, or other small invertebrates, to an
environmental factor
Prelab/skills: use of choice chamber or similar device.
Possible variables: light intensity, light wavelength, humidity, temperature, surface texture,
density of animals.
3. Investigate the effect of an environmental variable on the transpiration rate of a plant
Prelab/skills: use of potometer, mass changes in leaf as it dehydrates.
Possible variables: wind speed, leaf area, species of plant, light, humidity.
4. Plan an investigation on the effect of an abiotic factor on the rate of photosynthesis in water
plants
Prelab/skills: observation of gas release by plant (analysis of gas produced by photosynthesis),
method for collecting gas produced by photosynthesis or hydrogen carbonate indicator colour
change.
Possible variables: light intensity, light wavelength, temperature, species of plant, pH, salinity.
5. Investigate the factors affecting germination/seedling growth of a plant
Prelab/skills: testing for seed viability.
Possible variables: temperature, light, oxygen, pollutants, herbicides, salinity, seed age, species,
pH, nutrients.
6. Investigate the effect of physical or chemical factors on variations in the heart rate of water
fleas/annelid worms
Prelab/skills: immobilization of animal, location and observations of the animal's heart.
Possible variables: temperature, light, pH, electrolytes, alcohol, tobacco extract, pollutants,
pesticides.
7. Investigate the factors affecting either the movement of chloroplasts in Elodea leaf cells by
cytoplasmic streaming, or the beating of cilia on the gills of mussels
Prelab/skills: use of microscope and micrometer eyepiece, preparation of temporary mounts.
Possible variables: light, temperature, pH, salinity, electrolytes, pesticides, pollutants, tobacco
extract.
Further Suitable Investigations
1. Investigate factors affecting heart rate/blood pressure/breathing rate/CO2 production/skin
temperature/rate of sweat production
Prelab/skills: taking a pulse correctly, use of sphygmomanometer and spirometer, titrating the
acidity of NaOH solutions that absorb exhaled CO2.
Possible variables: exercise, age, fitness, gender, body mass/height, time of day, amount of
clothing.
2. Investigate a factor affecting fermentation rates of yeast
Prelab/skills: measuring rates of respiration by CO2 production or indicator colour change.
Possible variables: concentration of sugar, type of sugar, temperature, pH, inhibitors.
3. Investigate a factor affecting enzyme activity
Prelab/skills: measurement of enzyme activity—oxygen production by catalase, starch digestion
by amylase, fruit juice extraction by pectinase, protein digestion by bromelin in pineapples,
invertase activity in yeast.
Possible variables: substrate concentration, pH, temperature, inhibitor concentration, sources or
substrate (pectinase on different types or ripeness of fruit, catalase from different tissues).
4. Investigate a factor affecting the amount of vitamin C activity in foods
Prelab/skills: introduction to a test for the amount of vitamin C in a food.
Possible variables: sources (fruit juices, vegetables, milks), the effects of different methods of
food processing.
5. Plan an investigation on the hypothetical survival of caterpillars (using models made from
wool, pasta or modelling clay)
Possible variables: background colour, light intensity, collecting tools.
6. Investigate the effect of colour on human choice
Prelab/skills: unbiased testing methods.
Possible variables: background colour, intensity of colour, age, gender, effect on legibility,
learned associations (for example, with food types).
7. Plan an investigation on variations in the fitness of individuals
Prelab/skills: tests for fitness including muscle endurance, arm or shoulder power, leg power,
flexibility, speed and acceleration, body composition.
Possible variables: gender, height, mass, age, type of sport practised and intensity of practice.
Note: In addition to the candidate work, samples submitted for moderation must include the
instructions, written or oral, given to the candidate by the teacher. This vital information is used
by moderators to assess whether the experiment, as given, was suitable for assessing the criteria.
Biology teacher support material
Introduction
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Introduction
How to use this teacher support material
The design criterion in biology internal assessment
Errors and uncertainties in biology internal assessment
Manipulative skills in biology internal assessment
Assessed student work
Overview
Investigation 1
Investigation 2
Investigation 3
Investigation 4
Investigation 5
Investigation 6
Investigation 7
Investigation 8
© IBO 2007
The design criterion in biology internal assessment
Deciding upon the most suitable strategy for assessing the design criterion is one of the most
important decisions for the teacher of IB biology. The questions to be considered when
implementing a design strategy are as follows.
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How much lesson time is to be allocated to the task?
How many students are in the teaching group?
What resources are available for use by the students?
Will the task stimulate a spirit of inquiry in the students and promote thinking skills in
addressing a complex problem?
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Do the students have the prerequisite skills and knowledge base to address the task in a
meaningful manner?
Can the teacher ensure that, when completing the written phase of the investigation
outside the classroom, the students work independently?
The answers to these questions will differ according to each school’s environment, but some
common principles remain.
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Students are more likely to work independently if their design task is unique to them.
Students are more likely to work independently if they have confidence in their own
ability to complete the design task successfully.
Therefore, designing a scientific investigation is not done in a vacuum. For productive designs to
be developed, students will need to have:
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acquired some theoretical background
made observations in the relevant field
experience in certain manipulative skills specific to the investigation
an understanding of the scientific method (the importance of hypothesis testing where
relevant, replicates, independent and dependent variables, controls, and sampling
techniques).
Students will need to have their limits set, and counselling will be necessary in feasibility (time
and cost), safety and ethics. For the sake of assessment, the students should design their
investigations individually. However, it is possible that an initial brainstorming exercise would
be useful to generate ideas that could then be further developed by individuals. It is also possible
for students to work in groups on their proposed investigations after their individual designs
have been assessed.
To increase the students’ self-confidence in their ability to accomplish the task independently, it
is important for the design setting strategy to recognize that they need to have some familiarity
with the concepts and techniques involved. Some suggested approaches are as follows.
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Set tasks that are an advance on prior work. However, it is unethical to set students
essentially the same task as previously. Similarly, teachers should avoid setting a task
that is already covered completely in readily available literature such as the class’s
laboratory manual. It is not uncommon to find whole classes responding in an identical
manner to such a design task, which is not good practice. It is better to restrict such an
investigation to assessment of data collection and processing (DCP) and conclusion and
evaluation (CE).
Give students a practical session to familiarize themselves with the techniques required
prior to the writing up of their design. They can use this session to try out materials, to
develop a workable procedure, and to see if their proposed independent variable has any
measurable effect. If the teacher takes the opportunity during this session to ask the
students to explain their thinking behind what they are planning to do, then it is
subsequently easier to ascertain whether the student has produced the final written design
independently. Also, at this stage, students can be encouraged to think more deeply if
they are considering undertaking an over-simplistic investigation.
Once a teacher has decided on a strategy for assessing design, acceptable instructions then need
to be given to the students. Normally this takes the form of a brief description of the general
problem(s). Aspect 1 of the design criterion requires students to formulate the focused problem
or research question themselves, as well as to identify independent and dependent variables, and
relevant controlled variables. It is essential that the teacher gives the students only an open-ended
prompt, and the topic must allow for a variety of different approaches.
Most commonly, there are two types of appropriate teacher prompts.
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Firstly, where neither the dependent nor independent variables are given but the
system to be investigated is identified. An example of an open-ended teacher prompt
would be “Investigate an aspect of a pond ecosystem”.
The second type of prompt is where the dependent variable is given by the teacher and
the student must select the independent variable as well as appreciate the controlled
variables. An example here is when the teacher tells the student to investigate one factor
that affects the rate of photosynthesis in an aquatic plant.
It is wise to set a common task for all students only when the task potentially has many different
independent/control variables. For large class sizes, it can be difficult to ensure a wide enough
range of different approaches.
If a teacher is confronting the problem of many students simply reciting designs from web-based
sources or internal assessment (lA) manuals, it may be necessary to allocate supervised class
time to the task of writing up designs.
Inappropriate research questions often occur when the teacher prompts give both the dependent
and the independent variables, for example, “Investigate the relationship between wind speed
and the rate of transpiration of a leafy shoot”.
Investigations suitable for design
Students would be given only the titles of the following investigations. The titles are general to
give students a sense of direction, but they are not sufficiently specific to serve as a research
question. Students will have to modify the title to make it more specific and focused. In most
cases, this will involve indicating the variable(s) chosen for investigation and the species used or
the source of the biological material.
Investigations into abiotic factors and their effects
Investigate the effect of an abiotic factor on the distribution of an organism on a rocky shore or
stream.
PreA preliminary visit to make observations, identification of animals or plants, measurement of
lab/skill
variables, sampling techniques. The Biology guide (first exams 2009) indicates in page
s:
28:
Conclusion and evaluation
Aspect 1: concluding
When measuring an already known and accepted value of a physical quantity, students should
draw a conclusion as to their confidence in their result by comparing the experimental value with
the textbook or literature value. The literature consulted should be fully referenced.
Regarding uncertainties in prepared solutions may be the following information
helps to decide the way to indicate these uncertainties. They can be expressed as
a± ?%.
As stated in TSM (first exams 2009) Errors and uncertainties in biology internal assessment
http://xmltwo.ibo.org/publications/DP/Group4/d_4_biolo_tsm_0711_1/howToUse_
en.html
e) Degrees of precision and uncertainty in data
Students must choose an appropriate instrument for measuring such things as length, volume, pH and
light intensity. This does not mean that every piece of equipment needs to be justified, and it can be
appreciated that, in a normal science laboratory, the most appropriate instrument may not be available.
For the degrees of precision, the simplest rule is that the degree of precision is plus or minus (±
the
smallest division on the instrument (the least count). This is true for rulers and instruments with digital
displays.
The instrument limit of error is usually no greater than the least count and is often a fraction of the least
count value. For example, a burette or a mercury thermometer is often read to half of the least count
division. This would mean that a burette value of 34.1 cm3 becomes 34.10 cm3 (± 0.05 cm3). Note that the
volume value is now cited to one extra decimal place so as to be consistent with the uncertainty.
The estimated uncertainty takes into account the concepts of least count and instrument limit of error,
but also, where relevant, higher levels of uncertainty as indicated by an instrument manufacturer, or
qualitative considerations such as parallax problems in reading a thermometer scale, reaction time in
starting and stopping a timer, or random fluctuation in an electronic balance read-out. Students should do
their best to quantify these observations into the estimated uncertainty.
Other protocols exist for recording uncertainties. In biology internal assessment (IA), no specific protocol
is preferred, and a moderator will support a teacher when it is clear that recording of uncertainties has
been required and the uncertainties are of a sensible and consistent magnitude.
(f) Propagating errors
Propagating errors during data processing is not expected but it is accepted provided the basis of the
experimental error is explained.
In biology internal assessment (IA), no specific protocol is preferred, and a moderator
will support a teacher when it is clear that recording of uncertainties has been required
and the uncertainties are of a sensible and consistent magnitude.
Best regards,
Susana
Possible
Height above low water mark, density of other organisms, exposure to sea, current
variable
speed, light intensity, temperature.
s:
Investigate the behavioural responses of woodlice, or other small invertebrates, to an
environmental factor.
Possible
Light intensity, light wavelength, humidity, temperature, surface texture,
variables:
density of animals.
Investigate the effect of an environmental variable on the transpiration rate of a plant.
Pre-lab/skills:
Use of potometer, mass changes in leaf as it dehydrates.
Possible variables: Wind speed, leaf area, species of plant, light, humidity.
Design an investigation on the effect of an abiotic factor on the rate of photosynthesis in water
plants.
Observation of gas release by plant (analysis of gas produced by photosynthesis),
Premethod for collecting gas produced by photosynthesis or hydrogencarbonate
lab/skills:
indicator colour change, use of relevant probes with data logging apparatus.
Possible
Light intensity, light wavelength, temperature, species of plant, pH, salinity.
variables:
Investigate a factor affecting germination or seedling growth of a plant.
Pre-lab/skills:
Testing for seed viability.
Possible
Temperature, light, oxygen, pollutants, herbicides, salinity, seed age, species,
variables:
pH, nutrients.
Investigate the effect of a physical or chemical factor on variations in the heart rate of water fleas
or annelid worms.
Pre-lab/skills:
Immobilization of animal, location and observations of the animal's heart.
Temperature, light, pH, electrolytes, alcohol, tobacco extract, pollutants,
Possible variables:
pesticides.
Investigate a factor affecting either the movement of chloroplasts in Elodea leaf cells by
cytoplasmic streaming, or the beating of cilia on the gills of mussels.
Use of microscope and micrometer eyepiece, preparation of temporary
Pre-lab/skills:
mounts.
Light, temperature, pH, salinity, electrolytes, pesticides, pollutants, tobacco
Possible variables:
extract.
Further suitable investigations
Investigate a factor affecting either heart rate, blood pressure, breathing rate, CO2 production,
skin temperature or rate of sweat production.
Taking a pulse correctly, use of sphygmomanometer and spirometer, titrating the
Preacidity of NaOH solutions that absorb exhaled CO2, use of relevant probes with
lab/skills:
data-logging apparatus.
Possible
Exercise, age, fitness, gender, body mass/height, time of day, amount of clothing.
variables:
Investigate a factor affecting fermentation rates of yeast.
Measuring rates of respiration by CO2 production or indicator colour change, use
Pre-lab/skills:
of relevant probes with data-logging apparatus.
Possible
Concentration of sugar, type of sugar, temperature, pH, inhibitors.
variables:
Investigate a factor affecting enzyme activity.
Measurement of enzyme activity (oxygen production by catalase, starch digestion
Preby amylase, fruit juice extraction by pectinase, protein digestion by bromelin in
lab/skills: pineapples, invertase activity in yeast), use of relevant probes with data-logging
apparatus.
Substrate concentration, pH, temperature, inhibitor concentration, sources of
Possible
substrate (pectinase on different types of fruit, or on fruit of varying ripeness,
variables:
catalase from different tissues).
Investigate a factor affecting the amount of vitamin C activity in foods.
Pre-lab/skills:
Introduction to a test for the amount of vitamin C in a food.
Possible
Sources (fruit juices, vegetables, milks), the effects of different methods of
variables:
food processing.
Design an investigation on the hypothetical survival of caterpillars (using models made from
wool, pasta or modelling clay).
Possible variables:
Background colour, light intensity, collecting tools.
Investigate the effect of colour on human choice.
Pre-lab/skills: Unbiased testing methods.
Possible
Background colour, intensity of colour, age, gender, effect on legibility, learned
variables:
associations (for example, with food types).
Design an investigation on variations in the fitness of individuals.
Tests for fitness including muscle endurance, arm or shoulder power, leg power,
Pre-lab/skills:
flexibility, speed and acceleration, body composition.
Possible
Gender, height, mass, age, type of sport practised and intensity of practice.
variables:
Note: In addition to the student work, samples submitted for moderation must include the
instructions, written or oral, given to the student by the teacher. This vital information is used by
moderators to assess whether the experiment as given was suitable for assessing the criteria.
Manipulative skills in biology internal assessment
Manipulative skills are assessed summatively. This means that one overall mark will be given.
The skills assessed should cover most of the two-year course, and the mark given should reflect
the student’s general ability near the end of the course. This mark is not an average, nor does it
relate to a particular investigation. It is important therefore that the scheme of work sets the
students a variety of tasks and that they carry out a range of different techniques. The examples
below are suggestions to aid assessment of manipulative skills and are not considered to be a
prescribed list.
Note: No supporting evidence is required for moderation of manipulative skills.
Aspect 1: Following instructions
The student:
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reads/listens to instructions before asking for help
only starts the investigation after having read/listened to all the instructions
is able to follow a sequence of several written or verbal instructions with little assistance.
Aspect 2: Carrying out techniques
Measuring volume
The student:
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uses a suitable cylinder/pipette/burette size
does not overfill cylinder/pipette/burette
places his/her eyes at the height of the meniscus.
Measuring mass
The student:
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handles the balance appropriately when transporting it
adjusts the balance to zero before starting
uses a suitable container for the substance to be measured.
Measuring temperature
The student:
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leaves his/her thermometer in a safe place when it is not in use
refrains from touching the sides of the container with the thermometer when measuring
the temperature of a liquid
leaves the thermometer in the liquid when measuring the temperature of that liquid
stirs the liquid to obtain an even temperature estimate
reads the thermometer, avoiding parallax error.
Using a microscope
The student:
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removes microscope from storage, handling it without placing a strain on the focusing
mechanism
places the microscope on a bench in a clear stable area
starts observations on low-power objective using the coarse focusing control
adjusts light level to optimize contrast
returns microscope to storage area, clean, dry and on low power.
Calibrating a micrometer eyepiece
The student:
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places usual eyepiece in a safe place
cleans lens and slide micrometer of finger prints
replaces micrometer in its case.
Calibrating pH probe/colorimeter/oxygen probe
The student:
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selects appropriate solutions as blanks or standards
rinses probe with distilled water between different solutions.
Setting up a transect
The student:
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avoids disturbing the sampling area
lays out the guideline perpendicular to the environmental gradient being investigated if a
continuous transect is being used
makes regular measurements and observations along the transect using the same protocol.
Using a timed disturbance sampling in a stream
The student:
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avoids disturbing the sampling area before the net is in place
places the net facing the current downstream of the sample area
maintains a regular disturbance of the sampling area during the sampling time
lifts the net out of the water, keeping the mouth in the direction of the current.
Aspect 3: Working safely
The student:
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always wears safety clothing appropriate to the task, such as eye protection, lab coats and
gloves, when told to do so
pays proper attention to written or verbal safety instructions and hazard symbols
lights and uses the Bunsen burner safely
places flammable substances far away from flames
washes his/her hands after using toxic substances in the laboratory
takes care not to mix spatulas for different chemicals, or caps from different bottles
leaves glassware on solid surfaces and ensures that glassware cannot be knocked or rolled
off the surface
reports when equipment has been broken
avoids wasting chemicals and biological materials
cleans spilled chemicals or broken glassware immediately
washes and tidies up the equipment after use
disposes of chemicals in a suitable manner
shows awareness of the impact of chemicals on the environment
avoids using electrical apparatus near a water supply
follows safety guidelines when working in fieldwork.
Biology teacher support material
Introduction
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Introduction
How to use this teacher support material
The design criterion in biology internal assessment
Errors and uncertainties in biology internal assessment
Manipulative skills in biology internal assessment
Assessed student work
Overview
Investigation 1
Investigation 2
Investigation 3
Investigation 4
Investigation 5
Investigation 6
Investigation 7
Investigation 8
© IBO 2007
Errors and uncertainties in biology internal assessment
Biological systems are complex and difficult to control. Nevertheless, biological investigations
require measurements to be made, and biology students need to be aware of the sources of error
in their data, both qualitative and quantitative. For the purposes of internal assessment, work
assessed for data collection and processing must contain quantitative data suitable for processing.
The expectations with respect to errors and uncertainties in internal assessment are the same for
both standard level and higher level students, and are supportive of topic 1.1 of the subject guide.
The treatment of errors and uncertainties is directly relevant in the internal assessment of:
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data collection and processing, aspects 1 and 3 (recording raw data and presenting
processed data)
conclusion and evaluation, aspects 1 and 2 (concluding and evaluating procedure(s)).
Expectations at standard level and higher level
An appreciation of errors should be apparent at all stages of a report on an investigation:
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in the design stage, where the limitations of time and the materials should be assessed,
and the potential sources of error should be controlled. The magnitude and significance of
normal (background) variation in biological systems should be appreciated.
in the data collection and processing stage, where the degree of accuracy of a
measuring device should be stated as well as other observed sources of error
in the conclusion and evaluation stage, where the sources of error should be discussed,
along with possible ways of avoiding them.
Although students should analyse their investigations for sources of error, they should not be led
to conclude that, with all such sources of error and imprecision, experimental results are
worthless. Experimental results are only estimates.
Terms and concepts in error analysis
(a) Random variation or normal variation
In biological investigations, errors can be caused by changes in the material used, or by changes
in the conditions under which the experiment is carried out. Biological materials are notably
variable. For example, the water potential of potato tissue may be calculated by soaking pieces of
tissue in a range of concentrations of sucrose solutions. However, the pieces of tissue will vary in
their water potential, especially if they have been taken from different potatoes. Pieces of tissue
taken from the same potato will also show variations in water potential, but they will probably
show a normal variation that is less than that from samples taken from different potatoes.
Random errors can, therefore, be kept to a minimum by careful selection of material and by
careful control of variables. For example, you could use a water bath to reduce the random
fluctuations in ambient temperature.
Human errors can become random when people have to make a large number of tedious
measurements and, therefore, their concentration spans vary. Automated measuring, using a data
logger system, can help to reduce the likelihood of this type of error. Alternatively, the
experimenter can take a break occasionally.
(b) Human errors (mistakes)
Human errors can occur when tools, instruments or protocols are used or read incorrectly. For
example, a temperature reading from a thermometer in a liquid should be taken after stirring the
liquid and with the bulb of the thermometer still in the liquid. Thermometers (and other
instruments) should be read with the eye level with the liquid in the thermometer (reading
needle) to prevent parallax error. Human errors can be systematic, because the experimenter
does not know how to use the apparatus properly, or they can be random, because the power of
concentration of the experimenter is fading.
(c) The act of measuring
When a measurement is taken, this can affect the environment of the experiment. For example,
when a cold thermometer is put into a test tube with only a small volume of warm water in it, the
water will be cooled by the presence of the thermometer, or when the behaviour of animals is
being recorded, the presence of the experimenter may influence the animals’ behaviour.
(d) Systematic errors
Systematic errors can be reduced if equipment is regularly checked or calibrated to ensure that it
is functioning correctly. For example, a thermometer should be placed in an electronic water bath
to check that the thermostat of the water bath is correctly adjusted. A blank should be used to
calibrate a colorimeter to compensate for the drift of the instrument.
(e) Degrees of precision and uncertainty in data
Students must choose an appropriate instrument for measuring such things as length, volume, pH
and light intensity. This does not mean that every piece of equipment needs to be justified, and it
can be appreciated that, in a normal science laboratory, the most appropriate instrument may not
be available.
For the degrees of precision, the simplest rule is that the degree of precision is plus or minus (±)
the smallest division on the instrument (the least count). This is true for rulers and instruments
with digital displays.
The instrument limit of error is usually no greater than the least count and is often a fraction of
the least count value. For example, a burette or a mercury thermometer is often read to half of the
least count division. This would mean that a burette value of 34.1 cm3 becomes 34.10 cm3
(± 0.05 cm3). Note that the volume value is now cited to one extra decimal place so as to be
consistent with the uncertainty.
The estimated uncertainty takes into account the concepts of least count and instrument limit of
error, but also, where relevant, higher levels of uncertainty as indicated by an instrument
manufacturer, or qualitative considerations such as parallax problems in reading a thermometer
scale, reaction time in starting and stopping a timer, or random fluctuation in an electronic
balance read-out. Students should do their best to quantify these observations into the estimated
uncertainty.
Other protocols exist for recording uncertainties. In biology internal assessment (IA), no specific
protocol is preferred, and a moderator will support a teacher when it is clear that recording of
uncertainties has been required and the uncertainties are of a sensible and consistent magnitude.
(f) Propagating errors
Propagating errors during data processing is not expected but it is accepted provided the basis of
the experimental error is explained.
(g) Replicates and samples
Biological systems, because of their complexity and normal variability, require replicate
observations and multiple samples of material. As a rule, the lower limit is five measurements, or
a sample size of five. Very small samples run from 5 to 20, small samples run from 20 to 30, and
big samples run from 30 upwards. Obviously, this will vary within the limits of the time
available for an investigation. Some simple investigations permitting a large sample, or a large
number of replicate measurements, could be included in the scheme of work to reinforce this
point. It is also possible to use class data to generate sufficient replicates to permit adequate
processing of the data. However, each student must have been personally involved in the data
collecting process, and their own set of raw data should be presented and clearly identified.
Where sufficient replicates have been carried out, then the calculation of the standard deviation
of the mean is expected. Another statistic, the standard error of the mean to derive confidence
limits, may also be calculated. The standard error is not expected, but it would be an acceptable
alternative to the standard deviation.
In order to establish the significant difference between two samples, it may be possible to
calculate a student’s t-test. However, this would not be systematic as it is only appropriate to use
this statistic when certain conditions apply (interval data, sample sizes greater than five, normal
distribution of the population).
Where these statistics are calculated from a preset menu on a calculator or computer, a worked
example will not be expected, but the data should be presented in such a way that the steps in the
processing can be clearly followed.
Students should be made aware that, if a reading is particularly different from the others, it may
be left out of the processing and analysis. However, students must always justify why they have
chosen to do this.
Interpreting the relevant assessment criteria
Data collection and processing: Aspect 1 (recording raw data)
In tables of raw data, the degrees of precision of a measuring instrument should be given at the
head of a column along with the units (see part (e) above).
The number of decimal places in the raw data should agree with this degree of precision.
It may be that, in spite of extensive searching, the student does not have access to the degree of
precision of a measurement, for example, a solution prepared by a supply company or an
instrument that lacks technical specifications.
Tables 1–4 below show the raw data from an experiment that compared the behaviour of strips of
potato and apple tissues all cut to 4 cm long then soaked in different sucrose solutions.
Table 1: DCP aspect 1 = “complete”
Table 1
Lengths of two plant tissues, potato (Solanum) and apple (Malus) after soaking in solutions of
sucrose of different concentrations. The initial lengths were 4.0 cm.
Sucrose / mol dm–3 Potato lengths / cm ± 0.1 cm Apple lengths / cm ± 0.1 cm
0
4.2 4.0 3.9 4.0 4.2 4.2 4.3 4.1 4.3 4.4
0.2
4.0 3.8 4.2 4.1 4.1 4.1 4.2 4.2 4.1 4.2
0.4
3.8 3.7 3.7 3.7 3.8 4.1 4.2 4.3 4.2 4.2
0.6
3.8 3.7 3.7 3.8 3.6 4.0 4.0 4.1 4.1 4.0
0.8
3.6 3.5 3.7 3.7 3.5 4.1 4.0 3.9 3.9 4.0
1.0
3.7 3.6 3.7 3.7 3.6 3.8 4.0 4.0 3.8 3.9
It was also noticed before the soaking that the potato tissue floated in the solution from 0.4 to
1.0 mol. The apple tissue, however, only floated in the solutions from 0.6 to 1.0 mol. After
soaking the tissues became softer at higher sucrose concentrations but they were quite hard in the
lower concentrations.
The student has designed a table where the appropriate data are organized clearly with units and
uncertainties. The table has a precise title and there is relevant associated qualitative data
recorded.
Table 2: DCP aspect 1 = “partial”
Table 2
The lengths of potato and apple tissues after soaking.
Sucrose / mol dm–3 Potato lengths/ cm Apple lengths/ cm
0
4.2 4.0 3.9 4.0 4.2 4.2 4.3 4.1 4.3 4.4
0.2
4.0 3.8 4.2 4.1 4.1 4.1 4.2 4.2 4.1 4.2
0.4
3.8 3.7 3.7 3.7 3.8 4.1 4.2 4.3 4.2 4.2
0.6
3.8 3.7 3.7 3.8 3.6 4.0 4.0 4.1 4.1 4.0
0.8
3.6 3.5 3.7 3.7 3.5 4.1 4.0 3.9 3.9 4.0
1.0
3.7 3.6 3.7 3.7 3.6 3.8 4.0 4.0 3.8 3.9
The table contains appropriate quantitative data with units. The title is not very precise but would
be sufficient. However, there are no uncertainties and no associated qualitative data were
recorded.
Table 3: DCP aspect 1 = “partial”
Table 3
The results
Sucrose Potato lengths
Apple lengths
0
4.2 4 3.9 4 4.2 4.2 4.3 4.1 4.3 4.4
0.2
4 3.8 4.2 4.1 4.1 4.1 4.2 4.2 4.1 4.2
0.4
3.8 3.7 3.7 3.7 3.8 4.1 4.2 4.3 4.2 4.2
0.6
3.8 3.7 3.7 3.8 3.6 4 4 4.1 4.1 4
0.8
3.6 3.5 3.7 3.7 3.5 4.1 4 3.9 3.9 4
1
3.7 3.6 3.7 3.7 3.6 3.8 4 4 3.8 3.9
The table contains appropriate quantitative data. The title is inadequate but the data avoids total
ambiguity as it has correct column headings. There are no units or uncertainties given and no
associated qualitative data is recorded. The number of decimal places in the data is variable. This
is something that programs like MS Excel® do by default unless the student knows how to use
the control on the toolbar to set the number of decimal places. So even though a piece of tissue
may measure exactly 4 cm, it should still be recorded as 4.0 cm.
Table 4: DCP aspect 1 = “not at all”
Table 4
The results
Solution Potato measurements Apple measurements
0.2
4/3.8/4.2/4.1/4.1
4.1/4.2/4.2/4.1/4.2
0.4
3.8/3.7/3.7/3.7/3.8
4.1/4.2/4.3/4.2/4.2
0.6
3.8/3.7/3.7/3.8/3.6
4/4/4.1/4.1/4
0.8
3.6/3.5/3.7/3.7/3.5
4.1/4/3.9/3.9/4
1
3.7/3.6/3.7/3.7/3.6
3.8/4/4/3.8/3.9
Distilled water 4.2/4/3.9/4/4.2
4.2/4.3/4.1/4.3/4.4
The data is badly organized, there are no units or uncertainties, and it is too ambiguous to be
comprehensible.
Data collection and processing: Aspects 2 and 3 (processing raw data and presenting
processed data)
These two aspects will often be assessed on the same table or graph.
Processing data in biology often requires a statistical analysis of the data. This is because of the
inherent variability of the material used as well as variation due to its manipulation. Thus the
previous set of data (table 1) will possess an uncertainty because of the instrument used to
measure it (a millimetre ruler), the dexterity of the experimenter in cutting, and the variability in
the potato and apple tissue. A student could represent this by calculating a margin of error. The
simplest would be plus or minus the range of measurements or plus or minus half the range of
measurements. If the data permits, the error margin could be represented by plus or minus the
standard deviation of the mean or the standard error of the mean. These ranges may be expressed
as error bars on graphs. Though this is not obligatory, it would support assessment
statement 1.1.1.
Table 5: DCP aspect 2 = “partial”
Table 5 is from an investigation studying the effect of the colour of different light on the
movement of chloroplasts in Elodea leaf cells by cyclosis. It shows both raw data and processed
data. Here the appropriate units and degree of precision are given in the title of the column. The
rates of movement of the chloroplasts (this is the raw data in this case, as it is not calculated but
measured) are recorded to the appropriate number of decimal places. Unfortunately, the means
and standard deviations do not respect these degrees of precision. This student would only
achieve partial for aspect 2 of data collection and processing.
Table 5
Distance travelled by chloroplasts of Elodea canadensis in one minute while exposed to different
coloured light.
Speed of chloroplast / µm min–1
Trial
White Red Yellow Green Blue
1
12.6 9.3 14.1 10.6 8.6
2
10.3 6.4 11.6 10.2 11.7
3
9.9
7.5 13.4 12.5 8.2
4
11.1 8.1 12.1 10.8 7.0
5
9.1
9.5 10.4 11.0 9.9
6
10.8 9.8 10.6 12.5 9.7
7
10.0 8.3 10.4 9.7
6.4
8
10.2 8.4 9.9
10.3 8.7
9
10.5 8.4 11.6 11.0 7.0
Means
10.5 8.41 11.56 10.95 8.57
Standard deviations 0.972 1.054 1.436 0.968 1.682
The speed was estimated using a micrometer eyepiece calibrated for high power (×400) on the
microscope. At this magnification each graduation represented 2.6 µm.
Figure 1: DCP aspect 2 = “partial”
This processing could be accompanied by a bar chart (figure 1) to present the data more clearly.
Figure 1
The graph on its own would be awarded partial for aspect 2 of data collection and processing, as
the steps in the processing should be shown. The tabulated data would be required for a
“complete” for aspect 2. However, a worked example of the calculation of the mean or the
standard deviation would not be required, as these are part of a calculator statistics menu.
Figures 2 and 3: DCP aspect 2 = “partial”, aspect 3 = “complete”
Students who simply reproduce their raw data as graphs (as shown in figures 2 and 3) cannot
expect more than partial to be awarded for aspect 2 of data collection and processing. The
graphs in figures 2 and 3 show the data obtained from a field study of the distribution of a
species of flatworm in a stream.
Figure 2
Figure 3
Uncertainties are given where they are relevant, but the raw data has only been plotted on a
graph. The graphs are drawn using the correct conventions and they have a clear title. This would
permit the student to make some conclusions about the relationship between water temperature
and the distribution of the animal, but not with much certainty. The student would score partial
for aspect 2 and complete for aspect 3 of data collection and processing.
Figure 4: DCP aspect 2 = “complete”, aspect 3 = “complete”
Figure 4
Here the student has fully processed the data and would achieve complete for aspect 2 of data
collection and processing. The relationship between the variables is plotted as a scatter plot and a
trend line has been drawn. The computer program has also been used to calculate a correlation
coefficient, which can be used to back up the conclusion. It would not be expected for the
student to comment on the degree of significance of the correlation, just that it is high or low.
However, some appreciation that larger samples can have lower coefficients and still be
significant would be acceptable. For aspect 3 the correct conventions are used for the graph. It
has a clear title and uncertainties are shown through both the use of the trend line and the degree
of precision on the x-axis. This would be awarded a complete for aspect 3 of data collection and
processing.
Figure 5: DCP aspect 2 = “complete”, aspect 3 = “complete”
The set of graphs in figures 5–8 show a range of responses to the data given in table 1.
Figure 5
The graph is used to process the data by using the intercepts of the x-axis to establish the sucrose
solutions isotonic to the tissue sap. So long as this graph is accompanied by a table showing how
the data was processed, aspect 2 would achieve complete. Note in this method that the
calculation of the percentage change in length is not necessary as all the pieces of tissue were cut
to the same initial lengths (4.0 cm).
The graph is drawn using the correct conventions, it has a clear title and uncertainties are given
by the use of trend lines. The axes have graduations on them to improve the precision of the
estimate of the isotonic solutions. Aspect 3 has achieved complete.
Figure 6: DCP aspect 2 = “partial”, aspect 3 = “partial”
Figure 6
Though most of the processing has been done (calculating the average change in lengths),
unfortunately the comparison of the two tissues is not complete without the estimation of the
isotonic solutions. This can only be awarded partial for aspect 2.
The graph is drawn using correct conventions and the title is appropriate, but there are no
uncertainties given as degrees of precision, as error bars or as a trend line. Therefore only partial
can be awarded for aspect 3 of data collection and processing.
Figure 7: DCP aspect 2 = “partial”, aspect 3 = “partial”
Figure 7
Here there is evidence of some relevant processing (the average lengths), so the work can be
awarded partial for aspect 2 (again, a table showing the steps in processing would be required).
The graph is drawn using the correct conventions and it has an adequate title but there are no
uncertainties shown. Aspect 3 would be awarded partial.
Figure 8: DCP aspect 2 = “partial”, aspect 3 = “not at all”
Figure 8
Some processing has been carried out as expressed in the title, although the table shown in the
calculations would need to be given. This work can be awarded partial for aspect 2. The graph,
however, is incomprehensible. There are no labels on the axes and the key for the lines does not
reveal which tissue is which. As a result, not at all is awarded for aspect 3 of data collection and
processing.
Conclusion and evaluation
Errors and uncertainties are often relevant in aspects 1 and 2 of the conclusion and evaluation
criterion because students are expected to reach a reasonable and justified interpretation of the
data, and to appreciate the quality of the procedure (producing a measure of precision and
accuracy).
Aspect 1 (concluding)
After constructing the bar chart of the results of the effect of different coloured light on the
movement of chloroplasts in a leaf cell (table 5 and figure 1), the student makes the following
conclusion:
The fastest moving chloroplasts were in the green and yellow light, there is not a great difference
between these two colours. Both blue and red light produced the slowest movements. White light
lies in between the different colours.
The movement of the chloroplasts in leaves is necessary for them to orientate towards the light
for photosynthesis. It is also a way of mixing the cytoplasm so that substrates and products of
reactions are circulated. Photosynthesis shows peaks in its action spectrum and absorption
spectrum of the main chlorophyll pigments in the red and blue ends of the spectrum1. Therefore
chloroplasts illuminated with these colours would not have to move so much to absorb the same
amount of energy. The results for the different colours support this hypothesis. The error bars
suggest that there is a significant difference between the red light and the other colours. The data
for blue light, however, is not so different from the yellow and green. Unfortunately the data for
the control (white light) does not follow this pattern. If the hypothesis is right then the white light
illuminated chloroplasts should travel the slowest and they do not.
1
M.J. Farabee Photosynthesis (last update 2001) URL:
http://www.emc.maricopa.edu/faculty/farabee/BIOBK/BioBookPS.html
This conclusion with the appropriate reference sources cited would be awarded complete for
aspect 1 of conclusion and evaluation. Note that it may not be right but it is based upon evidence
that supports it. The level of the background that the student should be aware of in order to place
the conclusion in a correct scientific context will be up to the judgment of the teacher. But it
would be expected that the student does some independent background reading around the
appropriate subject area, for example, to establish literature values where available (for example,
in an investigation on the optimal pH of an enzyme).
For a partial for aspect 1 of conclusion and evaluation, the student wrote the following:
In green and yellow light the chloroplasts move faster but in blue and red light they move slower.
This supports the hypothesis that the movement of the chloroplasts is related to the rate of
photosynthesis in different wavelengths of light. In green and yellow light they need to capture
more energy for photosynthesis so they move faster to orientate themselves in the light.
The discussion of the data and what it reveals is superficial and no reference is made to any
control that may have been set up. Aspect 1 for conclusion and evaluation earns a partial.
A student who drew figure 6 above under aspect 3 of data collection and processing does not
construct a best straight line and makes an approximate estimate of the isotonic solution. Under
aspect 1 of conclusion and evaluation, the following earns a not at all.
The graph shows that the sucrose solution where the potato tissue does not change in size is
about 0.2 mol dm–3 whereas the solution where the apple tissue does not change in size is about
0.7 mol dm–3. Therefore the apple tissue contains more sucrose than the potato tissue.
Not only does the student ignore any variation in the data, but a serious error is made in the
conclusion. Sucrose is not necessarily the only solute in the tissue sap, if it is there at all.
Aspects 2 and 3 (evaluating procedure(s) and improving the investigation)
The evaluation of the investigation must take into account the weaknesses in the method that
undermine the conclusion that can be reached. The criticisms must also include weaknesses
observed in the data. If these variations in the data can be linked to observations made and noted
during the investigation (for example, in a log book), it reveals that the student is truly evaluating
the investigation and not desperately looking for something to say.
In the course of an investigation to investigate the diffusion of methylene blue stain through agar
gel, a student obtained the results and took the photograph displayed in figure 9, and made the
following statements in the evaluation based upon the quantitative and supporting qualitative
data.
Figure 9
The variations observed in the data could be due to the zones of different concentrations on
methylene blue overlapping and interfering with one another (as can be seen on the photograph).
A bigger dish or separate dishes of agar could be used to avoid this happening.
The shape of the diffusion zones is not always circular so the result will depend upon which way
the diameter is measured. This could have been due to inconsistencies in the gel, it was observed
that small bubbles were trapped in it and when it was cut using the cork borer the cracks
developed in the gel round the edges of the hole. A sharper cutting tool and a more uniform gel
would be better.
It was also difficult to tell exactly where the blue colour stopped as it faded into the surrounding
gel. So the measurements are a bit subjective. Good lighting and a uniform background are
recommended when measuring.
The range of concentrations was limited to 0.1–0.5%. A wider range may show whether the
straight line relationship is in fact a curve.
This evaluation would be awarded complete for both aspects 2 and 3 of conclusion and
evaluation. The criticisms made are based upon the measurements and observations made during
the investigation. The suggested improvements are realistic and would probably lead to
improved results. Note that these may not be the only criticisms that could be made, but an
exhaustive list is not what is expected.
A student who writes the following evaluation would only be awarded partial for aspects 2 and
3:
Cutting out the holes and removing the agar core was not efficient. Sometimes the core got stuck
and had to be taken out in pieces. A sharper corer should be used. It was difficult to measure out
the liquids precisely, a difference in the volume of stain may make a difference to the result. A
more precise graduated pipette should be used. Measuring the zone of diffusion on the base of
the dish was difficult.
This student’s evaluation is based on observed difficulties with the use of the equipment. These
are valid criticisms though some remain vague. The influence of limitations of the investigation
(for example, the range of concentrations used) is not considered. Aspect 2 of conclusion and
evaluation would be awarded partial.
The suggested improvements are valid but require a bit more detail. Not all of the weaknesses
identified have a suggested improvement. This would be awarded partial for aspect 3 of
conclusion and evaluation.
If the student evaluates as follows:
This experiment worked quite well. We might have got the pipettes mixed up when adding the
methylene blue. We might have made a mistake when measuring the diameter of the blue areas.
It could have been improved by using a more precise ruler, by being more careful with the
equipment and by doing it a few more times.
This type of conclusion reveals a student who is grasping at straws. He or she knows that to
fulfill the criterion one has to criticise and suggest improvements, but the criticisms made are not
backed up by an analysis of the errors in the data or observations made during the experiment.
Indeed, the suggestion of a more precise measuring device reveals confusion between the
concepts of precision and accuracy.
The weaknesses are not clearly identified and the suggested improvements are superficial. This
would be awarded not at all for aspect 2 and partial for aspect 3 of conclusion and evaluation.