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The Scien Þc Method

Lab 1

Lab 1 : Scien Þc Method

Introduc on

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 con nues to advance the understanding of the universe. Science begins with ob-

serva ons that can be measured in some way so that data can be collected in a useful manner by follow-

ing the scien Þc method.

Have you ever wondered why the sky is blue or why a plant grows toward a window? If so, you have al-

ready taken the Þrst step down the road of discovery. No ma er what the ques on, the scien Þc meth-

od can help Þnd an answer (or more than one answer!). Following the scien Þc method helps to insure

scien sts can minimize bias when tes ng a theory. It will help you to collect and organize informa on in

a useful way, looking for connec ons and pa erns in the data. As an experimenter, you should use the

scien Þc method as you conduct the experiments throughout this manual.

Concepts to explore:

Testable observa ons

Hypothesis

Null hypothesis

Experimental approach

Variables

Controls

Data collec on

Analysis

Figure 1: The process of the scien Þc method

Lab 1 : Scien Þc Method

The scien Þc method process begins with the formula on of a

hypothesis – a statement of what the experimenter thinks will

happen in certain situa ons. A hypothesis is an educated guess –

a proposed explana on for an event based on observa on(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 wri en in an if/

then format, such as:

Hypothesis:

If nutrients are added to soil, then plants grown in it will

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 o en many ways to test a hypothesis.

When designing an experiment to test a hypothesis

there are three rules to follow:

1. The experiment must be replicable.

2. Only test one variable at a me.

3. Always include a control.

Variables are deÞned and measurable components of an experiment. Controlling the variables in an

experiment allows the scien st to quan tate 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 scien st 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 a ects the outcome of the experi-

ment.

Dependent Variable: This variable is measured in regards to condi ons of the inde-

pendent variable—it depends on the independent variable. There can be more than

one dependent variable in each experiment.

If plants grow quicker when nutrients are added,

then the hypothesis is accepted and the null

hypothesis is rejected.

Figure 2: What a ects plant growth?

Lab 1 : Scien Þc 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

scien st is expec ng (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 iden Þed to

eliminate compounding changes that can inßuence the results. O en mes, the hardest part of design-

ing an experiment is not Þguring out how to test the one factor you focus on, but in trying to eliminate

the o en hidden inßuences that can skew results. Taking notes when conduc ng an experiment is im-

portant, whether it is recording the temperature, humidity, me of day, or another environmental con-

di on that may have an impact on the results. Also remember that replica on is fundamental to scien-

Þc experiments. Before drawing conclusions, make sure your data is repeatable. In other words, make

sure the experiment provides signiÞcant results over mul ple trials.

O en, 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 scien st should be able to pick up the table

or graph and have all of the informa on necessary to interpret it, with no other informa on.

Table: A well-organized summary of data collected. Only include informa on relevant to the hypothesis

(e.g. don’t include the color of the plant because it’s not relevant to what is being tested). Al-

ways include a clearly stated tle, label your columns and rows and include the units of meas-

urement. For our example:

Table 1: Plant Growth With and Without Added Nutrients

Graph: A visual representa on of the rela onship between the independent and dependent variable.

Graphs are useful in iden fying trends and illustra ng Þndings. Rules to remember:

The independent variable is always graphed on the x-axis (horizontal), with the depend-

ent variable on the y axis (ver cal).

Use appropriate numerical spacing when plo ng the graph, with the lower numbers

star ng on both the lower and le hand corners.

Always use uniform or logarithmic intervals. For example, if you begin by numbering, 0,

10, 20, do not jump to 25 then to 32.

Variable Height Wk1 (mm) Height Wk. 2 (mm) Height Wk. 3 (mm) Height Wk. 4 (mm)

Control

(without nutrients) 3.4 3.6 3.7

4.0

Independent

(with nutrients)

3.5 3.7 4.1 4.6

Lab 1 : Scien Þc Method

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 tled your table “Heart rate of those who eat veg-

etables and those who do not eat vegetables”, be sure to tle the graph the same.

Determine the most appropriate type of graph. Typically, line and bar graphs are the

most common.

Line graph: Shows the rela onship between variables using plo ed points that are connected with a

line. There must be a direct rela onship 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

Lab 1 : Scien Þc Method

Bar graph: Used to compare results that are independent from each other, as opposed to a con nuous

series. Since the results from our previous example are con nuous, they are not appropriate

for a bar graph.

Figure 4 shows the number of di erent kinds of birds observed on a hike. Since there is no rela onship

between the di erent types of birds each result is independent and a bar graph is appropriate.

Interpreta on: 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 varia-

bles which should be examined? For instance, was the amount of water and sunlight consistent be-

tween groups of plants or, were all types of birds equally likely to have been seen?

Figure 4: Number of Birds Seen on a Hike

Lab 1 : Scien Þc Method

Ac vity:

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 riv-

ers in order to support aqua c life. The dissolved oxygen is measure in units of ppm - or parts per mil-

lion. 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 ques ons below.

Table 2: Water Quality vs. Fish Popula on

1. Develop a hypothesis rela ng 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.

Dissolved Oxygen (ppm) 0

Number of Fish Observed 0