IB Biology Scientific Investigation Process
Name:__________
General Information
In this part of our IB Biology Course, we will explore the use of the scientific method in a deeper way than you ever have
before. To begin, we must understand the two basic types of Reason.
1. Inductive Reasoning:
-This type of reasoning/logic is the result of repeated observations that lead to a logical conclusion.
-Eg. Every time I drop an object, it falls to the ground.
-Therefore, there must be some attractive force causing the object to fall. (we know this as gravity).
-This type of reasoning trends from specific observations to a general hypothesis or scientific law.
2. Deductive Reasoning:
-This type of reasoning/logic takes some known hypothesis or scientific law (found through inductive reasoning)
and applies it to the prediction of specific events.
-Eg. If I drop my textbook, I predict- based on the law of gravity - that it will fall to the ground.
-This type of reasoning trends from general to specific.
**Scientific Investigations use both of these types of reasoning at different times. Any time we are collecting observations
with our senses about the natural world (whether in an experiment or not) in an attempt to create some overall hypothesis,
we are using inductive reasoning. Any time we make a prediction about what will happen based on some knowledge that
we think we have, we are using deductive reasoning.
Which type(s) of reasoning is illustrated in the two examples below?
1. All the swans I have ever observed are white. Therefore, all swans are white
2. Based on the Theory of Evolution, the DNA of Polar Bears should be extremely similar to that of the American
Black Bear.
An appreciation of the Power of Reason will become very important in the experiments that you will design and the data
which you will interpret in the next several months. One reason that the scientific method is such a powerful tool for
understanding the natural world is it’s acknowledgement of the potential for error and uncertainty associated with
empirical data. Empirical data is data that is collect through the senses (sight, hearing, smell, etc.) No other area of
knowledge takes as much time to limit the negative affects of error and uncertainty associated with these “weaknesses” as
does the scientific method.
LESSON 1 – The Scientific Method
Step 1: Observation
 Many types of observation can be made on biological systems. They may involve:
o observation of certain behaviors in wild populations
o physiological measurements made during previous experiments
o ‘accidental’ results obtained when seeking answers to completely unrelated questions
 The observations may lead to the formation of questions about the system being studied
In your group, make three observations of your biological sample:
1.
2.
3.
Step 2: Defining the Problem
Every scientific investigation begins with the question that the scientist wants to answer. The questions addressed by
scientific inquiry are based on observations or information gained through previous research. Just because a question can
be answered doesn’t mean that it can be answered scientifically.
Discuss the following questions with your group and decide which of them you think can be answered by scientific
inquiry.
 What is the cause of AIDS?
 Are serial killers evil by nature?
 Why is the grass green?
 What is the best recipe for chocolate chip cookies?
 When will the Big Earthquake hit San Francisco?
 How can the maximum yield be obtained from a peanut field?
 Does watching television cause children to have shorter attention spans?
How did you decide what questions can be answered scientifically?
Step 3: Identifying the Responding (Dependent) Variable
The dependant variable is what the investigator measures (or counts or records). It is what the investigator thinks will vary
during the experiment. For example, she may want to study peanut plant growth. One possible responding variable is the
height of the peanut plants.
Name some other aspects of peanut plants growth that can be measured:



All of these aspects of peanut plant growth can be measured and can be used as responding variables in an experiment.
There are different responding variables possible in an experiment. The investigator can choose the one she thinks is most
importance, or she can choose to measure more than one responding variable.
Step 4: Identifying the Independent Variable
The independent variable is what the investigator deliberately varies during the experiment. It is chosen because the
investigator thinks it might affect the responding variable.
Name some factors that might affect the numbers of peanuts produced by peanut plants:



In many cases, the investigator does not change the independent variable directly. She collects data and uses the data to
evaluate the hypothesis, rather than doing a direct experiment. For example, the hypothesis that more crimes are
committed during a full moon can be tested scientifically. The number of crimes committed is the responding variable and
can be measured from police reports. The phase of the moon is the independent variable. The investigator cannot
deliberately change the phase of the moon, but can collect data during any phase he chooses.
Although many hypotheses about biological phenomena cannot be tested by direct manipulation of a variable, they can be
evaluated scientifically by collecting data that could prove the hypothesis false. This is an important method in the study of
evolution, where the investigator is attempting to test hypotheses about events of the past.
The investigator can measure as many responding variables as she thinks are important indicators of peanut growth. By
contrast she must choose only one independent variable to investigate in an experiment. For example, if the scientist wants
to investigate the effect that the amount of fertilizer has on peanut growth, she will use different amounts of fertilizer on
different plants; the independent variable is mass of fertilizer.
Why is the scientist limited to one independent variable per experiment?
Time is frequently used as the independent variable. The investigator hypothesizes that the dependant variable will change
over the course of time. For example, he may want to study the diversity of soil bacteria found during different months of
the year. However, the units of time used may be anywhere from seconds to years, depending upon the system being
studied.
Identify the independent and dependant variables in the following examples.

Height of bean plants is recorded daily for 2 weeks.
Independent variable:______________________________________
Dependant variable:_______________________________________

Guinea pigs are kept at different temperatures for 6 weeks. Percent weight gain is recorded.
Independent variable:________________________________________
Dependant variable:_______________________________________

The diversity of algal species is calculated for a coastal area before and after an oil spill.
Independent variable:________________________________________
Dependant variable:_______________________________________

Light absorption by a pigment is measured for red, blue, green, and yellow light.
Independent variable:________________________________________
Dependant variable:_______________________________________
Step 5: Identifying the Controlled Variables
A third type of variable is the controlled variable. Controlled variables are factors that are kept equal in all treatments, so
that any changes in the responding variable can be attributed to the changes the investigator made in the independent
variable.
Materials used and measurement techniques are NOT controlled variables (they are validity measures). While materials
and techniques must be consistent, a true variable is something that could directly influence the responding variable, not
just how it is measured.
Since the investigator's purpose is to study the effect of one particular independent variable, he must try to eliminate the
possibility that other variables are influencing the outcome. This is accomplished by keeping the other variables at constant
levels, in other words, by standardizing these variables. For example, if the scientist has chosen the amount of fertilizer as
the independent variable, he wants to be sure that there are no differences in the type of fertilizer used. He would use the
same formulation and same brand of fertilizer throughout the experiment.
What other variables would have to be standardized in this experiment?
Step 6: Writing the Hypothesis
A hypothesis is simply a statement of the scientist’s educated guess at the answer to the question. The nature of science is
such that we can prove a hypothesis false by presenting evidence from an investigation that does not support the
hypothesis. But we cannot prove a hypothesis true. We can only support the hypothesis with evidence from this particular
investigation.
Scientific knowledge is thus an accumulation of evidence in support of hypotheses: it is not to be regarded as absolute
truth. Hypotheses are accepted only on a trial basis. Future investigations may be able to prove any hypothesis false.
Current scientific studies you read about in the newspaper (for example, investigations of the effects of caffeine) are
sometimes quite preliminary and therefore tentative in nature. Often, studies are published whose results contradict each
other. However, this does not mean that scientific knowledge is flimsy and unreliable. Much of the information in your
textbook, for example, is based upon many experiments carried out by numerous scientists over a period of time.
The scientific method, then applies only to hypotheses that can be proven false through experimentation. (There are other
types of scientific investigation, such as observation and comparison that do not involve hypothesis testing.) It is essential to
understand this in order to understand what is and is not possible to learn through science. Consider, for example, this
hypothesis: More people behave immorally when there is a full moon than at any other time of the month. The phase of
the moon is certainly a well-defined and measurable factor, but morality is not scientifically measurable. Thus there is no
experiment that can be performed to test the hypothesis.
Which of the following would be useful as scientific hypotheses?

Mice require calcium for developing strong bones.

Dogs are happy when you feed them steak.

An active volcano can be prevented from erupting by throwing a virgin into it during each full
moon.

The higher the intelligence of an animal, the more easily it can be domesticated.

HIV (human immunodeficiency virus) can be transmitted by cat fleas.
The investigator devises an experiment or collects data that could prove the hypothesis false. He should also think through
the possible outcomes of the experiment (whether the hypothesis is supported or proven false) and make predictions about
the effect of the independent variable on the responding variable in each situation. For example, a scientist has made the
following hypothesis: “Increasing the amount of fertilizer applied will increase the number of peanuts produced.” He has
designed an experiment in which different amounts of fertilizer are added to plots of land and the number of peanuts
yielded per plot is measured. The predictions should state specifically how the responding variable will change in relation to
the independent variable and must be stated as an “If … Then” statement. The general format for an “If … Then” statement
is “if the independent variable is changed in this way, then the dependent variable will change this way.” For example, if the
mass of fertilizer applied to a field is doubled, then the number of peanuts produced will double. Or, if the temperature of
the reactants in a chemical reaction increases, then the rate of the reaction will increase.
Write a hypothesis for each of the following:
 Guinea pigs are kept at different temperatures for 6 weeks. Percent weight gain is recorded.

Batches of seeds are soaked in salt solutions of different concentrations and the number of
seeds that germinate is counted for each batch.
Step 7: Setting the Levels of Treatment
Once the investigator has decided what the independent variable for an experiment should be, he must also determine how
to change or vary the independent variable. The values set for the independent variable are called the levels of treatment.
For example, an experiment measuring the effect of fertilizer on peanut yield has five treatments. In each treatment,
peanuts are grown on a 100-m2 plot of ground, and a different amount of fertilizer is applied to each plot. The levels of
treatment in this experiment are set as 200 g, 400 g, 600 g, 800 g, and 1000 g fertilizer/100 m2.
The investigator's judgment in setting levels of treatment is usually based on prior knowledge of the system. For example, if
the purpose of the experiment is to investigate the effect of temperature on weight gain in guinea pigs, the scientist should
have enough knowledge of guinea pigs to use appropriate temperatures. Subjecting the animals to extremely high or low
temperatures can kill them and no useful data would be obtained. Likewise, the scientist attempting to determine how
much fertilizer to apply to peanut fields needs to know something about the amounts typically used so he could vary the
treatments around those levels.
Step 8: Identifying the Control Group
It is also necessary to include control groups in an experiment. CONTROL GROUPS ARE DIFFERENT THAN CONTROLLED
VARIABLES. A control group is a treatment in which the independent variable is either eliminated or is set at a standard
value. The results of the control group are compared to the results of the experimental treatments. In the fertilizer
example, the investigator must be sure that the peanuts don't grow just as well with no fertilizer at all. The control would
be a treatment in which no fertilizer is applied. An experiment on the effect of temperature on guinea pigs, however,
cannot have a "no temperature" treatment. Instead, the scientist will use a standard temperature as the control and will
compare weight gain at other temperatures to weight gain at the standard temperature.
Tell what an appropriate control group would be for each of the following examples.
 The effect of light intensity on photosynthesis is measured by collecting oxygen produced by a
plant.

The effect of NutraSweet sweetener on tumor development in laboratory rats is investigated.
Step 9: Determining Replication
Another essential aspect of experimental design is replication. Replicating the experiment means that the scientist repeats
the experiment numerous times using exactly the same conditions to see if the results are consistent. Being able to
replicate a result increases our confidence in it. However, we shouldn't expect to get exactly the same answer each time,
because a certain amount of variation is normal in biological systems. Replicating the experiment lets us see how much
variation there is and obtain an average result from different trials.
A concept related to replication is sample size. It is risky to draw conclusions based upon too few samples. For instance,
suppose a scientist is testing the effects of fertilizer on peanut production. He plants four peanut plants and applies a
different amount of fertilizer to each plant. Two of the plants die.
Can he conclude that the amounts of fertilizer used on those plants were lethal? What other
factors might have affected the results?
In IB Biology, the minimum sample size for each level of treatment of the independent variable is 5.
Step 10: Writing the Method
After formulating a hypothesis and selecting the independent and responding variables, the investigator must find a
method to measure the responding variable; otherwise, there is no experiment. Methods are learned by reading articles
published by other scientists and by talking to other scientists who are knowledgeable in the field. For example, a scientist
who is testing the effect of fertilizer on peanuts would read about peanut growth and various factors that affect it. He
would learn the accepted methods for evaluating peanut yield. He would also read about different types of fertilizers and
their composition, their uses on different soil types, and methods of application. The scientist might also get in touch with
other scientists who study peanuts and fertilizers and learn about their work. Scientists often do this by attending
conferences where other scientists present results of investigations they have completed.
A group of students hypothesizes that the amount of alcohol produced in fermentation depends on the amount
of glucose supplied to the yeast. They want to use 5%, 10%, 15%, 20%, 25%, and 30% glucose solutions.

What is the independant variable?

What is the dependant variable?

What control group should be used?

What variables should be standardized?

How many levels of treatment are there?

What is the minimum total yeast samples that are needed to perform this experiment?
LESSON 2: Use/Manipulation of Scientific Instruments and Quantification of Error
Degrees of precision and uncertainty in data
PURPOSE:
To learn how to measure according to the IB standards.
HOW MANY DIGITS DO I INCLUDE?
o Unless there is a digital display, always measure to one spot beyond the smallest unit of CERTAIN measurement
of the tool.
o
For example, if you use a ruler that can accurately measure to the tenth of a centimeter, your measurement would
be to the hundredth of a centimeter.
This line
would be
measured as
45 mm
WHAT’S THE UNCERTAINTY?
 Every measurement has an uncertainty associated with it, unless it is an exact, counted integer, such as the
number of trials performed.
 The lower the accuracy and precision of a measurement instrument are, the larger the measurement uncertainty is.
 The numerical value of a ± uncertainty value tells you the range of the result. For example a result reported as 1.23
± 0.05 means that the experimenter has some degree of confidence that the true value falls in between 1.18 and
1.28
 To determine uncertainty:
o Find the smallest increment of measurement on your measurement device
This line would
be measured as
45 ±1 mm
CONVERSIONS
We’ll always and only use metric measurements in class. You should be familiar with the following metric prefixes and be
able to convert between them:
Kilok
103
1000
Hectoh
102
100
Dekada
10
10
BASE UNIT
----
Decid
10-1
0.1
Centic
10-2
0.01
Millim
10-3
0.001
Microµ (mu)
10-6
0.000001
Rotate from station to station to practice measuring with accuracy and determining uncertainty.
STATION
MEASURES
1
MEASURING
DEVICE
Mass balance
1
Metre Stick
Length
1
Ruler
2
Stop Watch
UNIT(S)
UNCERTAINTY
ITEM
MEASUREMENT
OF ITEM
CONVERT TO:
White beans
mg
Lobster
length
Leaf width
mm
Reaction
time (startstop)
ms
m
STATION
2
MEASURING MEASURES
DEVICE
Thermometer
UNIT(S)
UNCERTAINTY
ITEM
MEASUREMENT
OF ITEM
CONVERT TO:
Room
Nothing 
2
4
10ml
measuring
cylinder
25 ml
measuring
cylinder
50 ml
measuring
cylinder
100ml
measuring
cylinder
40ml beaker
4
80ml beaker
Water
L
4
200ml beaker
water
L
5
50ml flask
water
L
5
125ml flask
water
L
5
250ml flask
water
L
3
3
3
Volume
water
L
water
L
water
L
Water
L
Water
L
LESSON 3: Error Analysis
(a) 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.
(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) 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.
In the experiment designed to test the affect of different types of mediation on heart rate where the
types of mediation were:
1) breathing meditation
2) music meditation
5 pupils were used in the experiment
Discuss possible sources of error for each of the types of error and describe ways you could minimize the error
Type of Error
Normal Variation
Human errors
(mistakes)
The act of measuring
Systematic errors
Replicates and
samples
Possible Error
Way(s) to Minimize
Statistics is a tool used to answer general questions on the basis of a limited amount of specific data. Statistics allows us to
make decisions about a population based on a sample of that population rather than on the entire population.
Consequently, the use of statistics enables us to make decisions even though we have incomplete data about a population.
Although this may seem unscientific, we do it all the time; for example, we diagnose disease with a drop of blood (not a
persons entire blood supply). Statistics-based decisions are necessary when it is impossible or unrealistic to analyze an
entire population.
Let’s say that you want to know the lipid content of a typical corn grain. To obtain this information, you could analyze one
grain, but how would you know that you’d picked a “typical” grain? After all, the silo of seeds from which you chose the
grain my have contained millions of other grains, each a little bit different. You’d get a better estimate of “typical” if you
increased you sample size to a few hundred grain, or even to 10,000. Or better yet, to 1,000,000. Better yet….
The only way to be certain your conclusions would be to measure all of the corn grains in the world. Since this is clearly
impossible, you must choose grains that represent all of the grains in the world – that is, you must be working with a
representative sample. A statistical analysis of those sample grains would reduce the sample to a few characteristic
measurements (for example, mean percentage lipid). As you increase the size of the sample, these characteristic
measurements provide an ever-improving estimation of what is “typical.”
There are a variety of software programs that perform statistical analysis of data; all you have to do it enter your data into a
spreadsheet, select the data you want to analyze, and perform the analysis. Although these software programs save time
and can increase accuracy, you still need to understand a few of the basic tests that you’ll use to understand your numerical
data.
 The mean is the arithmetic average of a group of measurements. Chance errors in measurements tend to cancel
themselves when means are calculated; a value that is too high because of random error is often balanced by a
value that is too low for the same reason.
 The median is the middle value of a group of measurements. The median is less sensitive to extreme values than is
the mean.
Consider a sample consisting of 14 shrimp having the following lengths (all in mm):
80 69 62 74 69 50 45 40 9 64 65 64 61 67
1. Calculate the mean length.
2. Does the mean describe the “typical” shrimp length? Why or why not?
3. Determine the median by arranging the measurements in numerical order and finding the middle value.
4. In this sample, what is responsible for the difference between the mean and median?
5. How would the median change if the 9mm long shrimp was not in the sample?
6. How would the mean change if the 9mm long shrimp was not in the sample?
Consider these samples:
Sample 1: 25 35 32 28
Sample 2: 15 75 10 20
7. What is the mean for sample 1? For sample 2?
As you can see, the samples values are different, but their means are the same. Thus, the mean does not reveal all there is
to know about these samples. To understand how these samples are different, you need other statistics.
The range is the difference between the extreme measurements (smallest and largest) of the sample. The range provides a
sense of the variation of the sample.
8. What is the range of sample 1? Of sample 2?
The variation of the data about the mean is called the standard deviation. You’ll learn more about this later.
Summarise your key words! Join the correct word with its definition
Variable
Completed before the main investigation to decided
upon the details of the experiment.
Continuous data
Data that is measured on a scale. Examples could be
size, weight and diameter of holes.
Dependent variable
The variable that we keep the same to make the
investigation a fair test.
Reliable
Observation in data that is far removed in value from
the other data readings.
Results that are close to each other. Related to using
small scale divisions on measuring instruments.
Categorical data
Precise
Actions we take to keep the investigation under equal
conditions
Anomalous result
Type of graph drawn with categoric data.
Fair Test
The variable that you measure and record.
Independent variable.
The graph you draw with continuous data.
Control variable
Something that you can change or keep the same
during an investigation.
Best fit line graph
Data that can be put into categories such as gender
(males and female) and colour.
Mean
The variable that you change in an investigation.
Bar Chart
The results that we can trust are called this. It is more
likely to be this if repeats and a mean are taken.
Preliminary experiments
The average.