This is the power point that we used in class to learn the Nature of Science terms.

1. Science
• Systematic knowledge of the physical or material world
gained through observation and experimentation.

2. Observation
• Consists of receiving knowledge of the outside world through our
senses, or recording information using scientific tools and instruments.
• Example:
Once dropping the M&M into the water, the color of the M&M spread
into the water in the shape of a circle around the M&M. The circle
continued to get larger the longer the M&M sat in the water. The M&M
itself turned white.

3. Questions can be divided into two
categories: Causal and Existence.
• Scientists ask causal questions when trying to
understand the world.
• Causal Questions are Testable.

4. Causal Questions are Testable
• Testable questions begin with: How, What, If, Does and I
Wonder.
• These questions can be addressed through scientific
experiments.
• Examples:
– What color M&M dissolves fastest in water?
– Does temperature of the water change the dissolving
rate of an M&M?

5. Testable Questions…
• ask about objects, organisms, and events in the
natural world.
• can be answered through investigations that involve
experiments, observations, or surveys.
• are answered by collecting and analyzing evidence
that is measurable.
• relate to scientific ideas rather than personal
preference or moral values.
• do not relate to the supernatural or to non-
measurable phenomena.

6. Existence Questions – not usually testable
• Usually begin with “Why” and generally require recall
of factual information.
• Examples:
– Why does the candy coating on an M&M dissolve?
– Why does hot water cause the M&M to dissolve
faster?

7. Hypothesis
• A tentative explanation for an observation, phenomenon, or
scientific problem that can be tested by further
investigation.
• Example:
The brown M&M will dissolve faster than the other colors
because brown is a mixture of all the other colors so it has
more color and will therefore dissolve faster.

8. Variables
• factors or elements that are likely to vary or change.
• A typical study has an independent variable and a
dependent variable.

9. What’s the difference?
• The independent (or manipulated) variable is something
that the experimenter purposely changes or varies over
the course of the investigation.
• The dependent (or responding) variable is the one that is
observed and likely changes in response to the
independent variable.
• So… in our M&M experiment where we were trying to
answer the testable question, “Do some M&M colors
dissolve faster than others?” what was the independent
variable (what did we purposefully change)?
• What was the dependent variable (what did we observe or
measure)?

10. Identifying variables in an experiment:
• For example, a student might change the position of a
plane’s wing to see how it affects the average speed of a
model plane.
• What would be the independent variable?
• Dependent variable?
Independent Variable: Dependent Variable:
The wing position The average speed since
because the student the average speed would
purposely changes its depend on the location of
location the wing and it’s what’s being
observed or measured

11. To summarize variables…
• In an experiment, one variable is changed
(independent) and a second variable is measured in
response (dependent).

12. Controlling Variables
• When conducting an experiment, all other variables must be
kept the same throughout the investigation; they should be
controlled. The variables that are not changed are called
controlled variables.
• Example:
– When we tested “Does water temperature affect the dissolving
speed of M&Ms?” we kept everything else the same (color of M&M,
amount of water, type of dish, not stirring or disturbing either
place…) except the water temperature.

13. Control Group
• Group separated from the rest of the experiment
where the independent variable being tested cannot
influence the results.
• Using a control group enables us to study the impact
of the independent variable.
• Example:
– The control group in our M&M experiment “Does temperature
of water change the dissolving speed?” our control group was
the M&M we placed in room temperature water.

14. Data
• Data are your recorded observations and
measurements taken during an experiment.
• Data tables are usually used to organize data.
• Data can be qualitative (descriptive) or quantitative
(numerical)
Color of M&M Time for color ring to reach
“finish line”
Blue 90 seconds
Green 85 seconds
Brown 91 seconds

15. In general, data tables should have the
following format:
Independent Variable Dependent Variable (What you Measure) Average of the Trials
(What you modify)
Trial 1 Trial 2 Trial 3
So, for our M&M experiment it would look like:
Color of M&M Time for color to reach “finish line” Average of the Trials
Trial 1 Trial 2 Trial 3
Blue 90s 91s 89s 90.0s
Green 90 90 89 89.7s
Brown 87 90 92 89.7s

16. Technology is often used in science to
help measure & collect data.
• What technology could we have used in our M&M
experiment that would have given us more accurate
data?

17. Why & how to graph data
• Graphs represent data in a visual, easy to read manner, which
helps us to understand data more clearly
• Independent variable should be placed along the bottom of the x-
axis.
• Dependent variable should be placed on the side of the y-axis.
• Label the axes — don't forget to include the units of
measurement (grams, centimeters, liters, etc.).
• If you have more than one set of data, show each series in a
different color or symbol and include a legend with clear labels.

18. Different Types of Graphs - Bar
• Bar Graph used when comparing different trials or
different experimental groups. It also may be a good
choice if your independent variable is not numerical.

19. Different Types of Graphs - line
• Line Graph is a good way to look at how something
changes, usually over time or sometimes across space.

20. Different Types of Graphs - Pie
• Pie Graph is the best way to show portions, or parts
of a whole. Using this pie graph, we can see just what
portion of all the trash each particular type of trash
represents.

21. Different Types of Graphs – Scatter Plot
• Similar to line graphs
• Show how much one variable is affected by another. The
relationship between two variables is called their
correlation .
• The closer the data points come to making a straight line,
the higher the correlation between the two variables, or the
stronger the relationship.

22. Scatter Plot - continued

23. Best Fit Line - used with scatter plots
• Drawn through a scatter plot to find the direction of an association
between two variables. This line of best fit can then be used to make
predictions.
• To draw a line of best fit, balance the number of points above the line
with the number of points below the line
• Is association positive or negative?
• Is association weak or strong?
•Use the line of best fit to predict the swimming pool attendance where
the daily maximum temperature is:
(i) 18 ºC (ii) 30 ºC (iii) 40 ºC

24. Inference
• Inference is just a big word that means judgement.
• If you infer that something has happened, you do not see,
hear, feel, smell, or taste the actual event. But from what you
know, it makes sense to think that it has happened.
• You make inferences everyday. Most of the time you do so
without thinking about it.
• Suppose you are sitting in your car stopped at a red signal
light. You hear screeching tires, then a loud crash and
breaking glass. You see nothing, but you infer that there has
been a car accident.
• Example:
– On your way to your next class after conducting the M&M
experiment, you notice red color on your hand and pencil. The M&M
you tested was red. You infer that the red color from the M&M
dissolved in the sweat and oils on your hand.

25. Conclusion
• The answer to a testable question that is supported by the
evidence collected (data).
• Example:
– No one color of M&M dissolves faster than the others. Six
different groups tested the various colors and each group found a
different color to dissolve faster. In order to be more sure of
this conclusion, more trials would need to be conducted.

26. Reliability & Validity
• An experiment is considered reliable if other researchers
are able to perform exactly the same experiment, under
the same conditions and generate the same results. This
will reinforce the findings and ensure that the wider
scientific community will accept the conclusion. Multiple
trials improve reliability.
• Validity encompasses the entire experimental concept and
establishes whether the results obtained meet all of the
requirements of the scientific research method. Valid
experiments control all the variables except the one
being tested, precisely measure and record data,
accurately display the data, develop a conclusion based
on the data.