Lab 1
The ScienƟÞc Method
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Lab 1 : ScienƟÞc Method
Concepts to explore:
x
x
x
x
x
x
x Data collecƟon
x Analysis
Testable observaƟons
Hypothesis
Null hypothesis
Experimental approach
Variables
Controls
IntroducƟon
What is science? You have likely taken several classes throughout your career as a student, and know
that it is more than just chapters in a book. Science is a process that uses evidence to understand the
history of the natural world and how it works. It is constantly changing as we understand more about
the natural world, and conƟnues to advance the understanding of the universe. Science begins with observaƟons that can be measured in some way so that data can be collected in a useful manner by following the scienƟÞc method.
Have you ever wondered why the sky is blue or why a plant grows toward a window? If so, you have already taken the Þrst step down the road of discovery. No maƩer what the quesƟon, the scienƟÞc method can help Þnd an answer (or more than one answer!). Following the scienƟÞc method helps to insure
scienƟsts can minimize bias when tesƟng a theory. It will help you to collect and organize informaƟon in
a useful way, looking for connecƟons and paƩerns in the data. As an experimenter, you should use the
scienƟÞc method as you conduct the experiments throughout this manual.
Figure 1: The process of the scienƟÞc method
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Lab 1 : ScienƟÞc Method
The scienƟÞc method process begins with the formulaƟon of a
hypothesis – a statement of what the experimenter thinks will
happen in certain situaƟons. A hypothesis is an educated guess –
a proposed explanaƟon for an event based on observaƟon(s). A
null hypothesis is a testable statement, that if proven true means
the hypothesis was incorrect. Both statements must be testable,
but only one can be true. Hypotheses are typically wriƩen in an if/
then format, such as:
Hypothesis:
If nutrients are added to soil, then plants grown in it will
Figure 2: What aīects plant growth?
grow faster than plants without added nutrients in the soil.
Null hypothesis:
If nutrients are added to the soil, then the
plants will grow the same as plants in soil
without added nutrients.
There are oŌen many ways to test a hypothesis.
When designing an experiment to test a hypothesis
there are three rules to follow:
If plants grow quicker when nutrients are added,
then the hypothesis is accepted and the null
hypothesis is rejected.
1. The experiment must be replicable.
2. Only test one variable at a Ɵme.
3. Always include a control.
Variables are deÞned and measurable components of an experiment. Controlling the variables in an
experiment allows the scienƟst to quanƟtate the changes that occur so that results can be measured
and conclusions drawn. There are three types of variables:
Independent Variable: The variable that the scienƟst changes to a predetermined value
in order to test the hypothesis. There can only be one independent variable in each
experiment in order to pinpoint the change that aīects the outcome of the experiment.
Dependent Variable: This variable is measured in regards to condiƟons of the independent variable—it depends on the independent variable. There can be more than
one dependent variable in each experiment.
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Lab 1 : ScienƟÞc Method
Controlled Variable: This variable, or variables (there could be many) reßect the factors
that could inßuence the results of the experiment, but are not the planned changes the
scienƟst is expecƟng (by changing the independent variable). These variables must be
controlled so that the results can be associated with some change in the independent
variable.
When designing the experiment, establish a clear and concise procedure. Controls must be idenƟÞed to
eliminate compounding changes that can inßuence the results. OŌen Ɵmes, the hardest part of designing an experiment is not Þguring out how to test the one factor you focus on, but in trying to eliminate
the oŌen hidden inßuences that can skew results. Taking notes when conducƟng an experiment is important, whether it is recording the temperature, humidity, Ɵme of day, or another environmental condiƟon that may have an impact on the results. Also remember that replicaƟon is fundamental to scienƟÞc experiments. Before drawing conclusions, make sure your data is repeatable. In other words, make
sure the experiment provides signiÞcant results over mulƟple trials.
OŌen, the best way to organize data for analysis is as a table or a graph. Remember, any table or graph
should be able to stand on its own. In other words, another scienƟst should be able to pick up the table
or graph and have all of the informaƟon necessary to interpret it, with no other informaƟon.
Table: A well-organized summary of data collected. Only include informaƟon relevant to the hypothesis
(e.g. don’t include the color of the plant because it’s not relevant to what is being tested). Always include a clearly stated Ɵtle, label your columns and rows and include the units of measurement. For our example:
Table 1: Plant Growth With and Without Added Nutrients
Variable
Height Wk1 (mm)
Control
(without nutrients)
Independent
Height Wk. 2 (mm) Height Wk. 3 (mm) Height Wk. 4 (mm)
3.4
3.6
3.7
3.5
3.7
4.1
4.0
4.6
(with nutrients)
Graph: A visual representaƟon of the relaƟonship between the independent and dependent variable.
Graphs are useful in idenƟfying trends and illustraƟng Þndings. Rules to remember:
x
The independent variable is always graphed on the x-axis (horizontal), with the dependent variable on the y axis (verƟcal).
x
Use appropriate numerical spacing when ploƫng the graph, with the lower numbers
starƟng on both the lower and leŌ hand corners.
x
Always use uniform or logarithmic intervals. For example, if you begin by numbering, 0,
10, 20, do not jump to 25 then to 32.
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Lab 1 : ScienƟÞc Method
x
Title the graph and both the x and y axes such that they correspond to the table from
which they come. For example, if you Ɵtled your table “Heart rate of those who eat vegetables and those who do not eat vegetables”, be sure to Ɵtle the graph the same.
x
Determine the most appropriate type of graph. Typically, line and bar graphs are the
most common.
Line graph: Shows the relaƟonship between variables using ploƩed points that are connected with a
line. There must be a direct relaƟonship and dependence between each point connected.
More than one set of data can be presented on a line graph. Figure 3 uses the data from
our previous table:
Figure 3: Plant Growth, with and without Nutrients, over Time
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Lab 1 : ScienƟÞc Method
Bar graph: Used to compare results that are independent from each other, as opposed to a conƟnuous
series. Since the results from our previous example are conƟnuous, they are not appropriate
for a bar graph.
Figure 4: Number of Birds Seen on a Hike
Figure 4 shows the number of diīerent kinds of birds observed on a hike. Since there is no relaƟonship
between the diīerent types of birds each result is independent and a bar graph is appropriate.
InterpretaƟon: Based on the data you collected, is your hypothesis supported or refuted? Based on the
data, is the null hypothesis supported or refuted? If the hypothesis is supported, are there other variables which should be examined? For instance, was the amount of water and sunlight consistent between groups of plants or, were all types of birds equally likely to have been seen?
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Lab 1 : ScienƟÞc Method
AcƟvity:
Dissolved oxygen is oxygen that is trapped in a ßuid, such as water. Since virtually every living organism
requires oxygen to survive, it is a necessary component of water systems such as streams, lakes and rivers in order to support aquaƟc life. The dissolved oxygen is measure in units of ppm - or parts per million. Examine the data in Table 2 showing the amount of dissolved oxygen present and the number of
Þsh observed in the body of water the sample was taken from; Þnally, answer the quesƟons below.
Table 2: Water Quality vs. Fish PopulaƟon
Dissolved Oxygen (ppm)
0
2
4
6
8
10
12
14
16 18
Number of Fish Observed
0
1
3
10 12
13
15
10
12 13
1. Develop a hypothesis relaƟng to the amount of dissolved oxygen measured in the water
sample and the number of Þsh observed in the body of water.
2. What would your experimental approach be to test this hypothesis?
3. What are the independent and dependent variables?
4. What type of graph would be appropriate for this data set? Why?
5. Graph the data from Table 2.
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