What is the Nature of Science?
The Nature of Science is a logical,
sequential way of investigating our world.
 We wonder, what would happen if I …?
 Then we devise a scientific investigation
to explore this idea.
 Scientific investigations have required
parts, and a required order.
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Variables
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Variables are the components that change in a
scientific investigation. The components must
be measurable. There are 2 types of variables:
◦ The independent variable is the component that the
investigator changes. It is graphed on the x axis.
There is only 1 independent variable.
◦ The dependent variable is the component that
changes due to the independent variable. It is graphed
on the y axis. There is only 1 dependent variable.
Constants
In a valid scientific investigation, we
change 1 variable (independent) and
measure the effect on 1 other variable
(dependent).
 All other components must remain the
same!
 Components that don’t change in a
scientific investigation are called constants.
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Constants - 2
For example, we might investigate how amount
of sunshine affects plant growth.
 We would change the daily amount of sunshine
(independent variable) and measure the amount
of plant growth (dependent variable).
 What would some constants be?
 Amount of water, type of plant, type of soil,
temperature of the environment, etc – all must
stay the same!

Control
But we would also need to know if
sunshine affects plant growth at all, so we
need a control – in which we measure the
dependent variable when the independent
variable = 0.
 For this experiment, the control would be
the amount of growth for a plant with no
daily sunshine.
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Hypothesis
A hypothesis is a statement that links
the independent to the dependent
variable.
 It is often written in this form: If the
independent variable does this, then the
dependent variable will do this.

Hypothesis - 2
For our earlier experiment (amount of
sunshine and plant growth), an acceptable
hypothesis would be:
 If the amount of sunshine increases, the
amount of plant growth will increase.

Hypothesis - 3
What would be another valid hypothesis?
 If the amount of sunshine increases, the
amount of plant growth will decrease.
 Or
 If the amount of sunshine increases, the
amount of plant growth will remain
unchanged.

Hypothesis - 4

2 purposes for a hypothesis:
◦ To get you thinking about the experiment
◦ To get you invested in the outcome

A hypothesis is NOT judged on
correctness – it is unacceptable to go
back and change your hypothesis to
reflect what actually happened!
Data
Data is collected through observation –
using 1 or more of the 5 senses.
 Examples of observation:
◦ seeing the volume in a graduated
cylinder
◦ smelling the sulfur odor from a chemical
◦ hearing the tick of the metronome, etc.

Analysis
Anything done to the data is analysis.
 Analysis includes:
◦ graphing
◦ identifying trends
◦ making calculations
◦ estimating amount and types of error,
etc.

Graphing
Types of graphs and common uses:
 A circle graph is for percentages.
 A bar graph is for data that occurs in
categories (grades, months, m/f, etc) –
called “discrete” data.
 A line graph is for continuous data.

Graphing - 2
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A correct line graph has:
a relevant title,
each axis is labeled including units,
each axis has a consistent scale,
points are plotted,
a line or curve of best fit is drawn (going thru as
many points as possible, and with as many
points above the line as below)
Graphing - 3
If the data points appear to be linear, graph it as
a line of best fit.
 If the data points appear to be curved, graph it
as a smooth curve of best fit.
 Since we are looking for trends or patterns,
very rarely do we “connect the dots” when
graphing in science!

Identifying trends

Trends are either:
◦ Direct relationship – when one value increases
the other value also increases
 or 
or a line with a positive slope
◦ Inverse relationship – when one value increases
the other value decreases

or a line with a negative slope
• No relationship – either too varied to be
determined, or remains constant (a line with 0
slope)
Making calculations
Suppose your task is to find the density of
an object. Your lab equipment can measure
mass and volume. You can calculate density
as mass/volume. Mass and volume are data,
the calculation for density is analysis (since
you didn’t directly observe it).
 Often we graph linear data and calculate the
slope of the line.
 Slope = (y2 – y1)/(x2 – x1)
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Making calculations - 2
What is the slope of this line?
Making calculations - 3
The equation for a line is y = mx + b
 m is the slope, and b is the y-intercept.
 What would be the equation for the
previous graph?
 y = (.00625 kgm-2/mm)x + .13kgm-2
 What is y measuring?
 What is x measuring?


Cucumber yield = (.00625 kgm-2/mm)precipitation + .13kgm-2
Estimating Error
Measurement errors can be categorized
as 2 types:
1. Random – caused by the person making
the measurement. Random errors can be
reduced by repeating the measurement
and taking the average.
2. Systematic – caused by the system or
equipment used to make the
measurement.

Estimating Error - 2
Ways we will calculate:
 % error is used when comparing an
experimental value to a known, standard
theoretical value (such as atomic mass,
acceleration due to gravity):

◦ % error = (|theo – exp| / theo) x 100

% difference is used when comparing 2
experimental values:
◦ % diff ={|val 1 – val 2| / [1/2 (val 1 + val 2)]} x 100
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Handout: Calculating uncertainties for IB
Estimating Error - 3
You found carbon’s mass to be 11.5 amu.
Your textbook lists it as 12.0 amu. What
is the % error?
 4.2 %
 You measured an object’s mass as 25.7 g
and your lab partner measured it as 26.9 g.
What is the % difference?
 4.6 %
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Human Error Activity
6 stations each with a designated task
 Perform each task, record your results
 For each station, calculate % difference
between your value and Mrs. G’s value
 Calculate an overall average of your
differences
 Don’t turn it in yet! Be ready to share!
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