1. Scientific Inquiry and the Scientific Method
Understanding the World Around Us

2. Vocabulary Introduction
Observation (Facts)
Definition
Examples
Observations/Facts you make with your senses that you know to be true.
Quantitative: numbers
Qualitative: descriptions that cannot be put in numbers

3. Vocabulary Introduction
Inferring
Definition
Examples
An explanation or interpretation of observations.
Inferences are based on reasoning, not random guessing

4. Vocabulary Introduction
Prediction
Definition
Examples
A forecast of what will happen in the future
Based on past evidence or observations.

5. Vocabulary Introduction
Hypothesis
Definition
Examples
“An educated guess”
A statement of what you intend to investigate
Written as follows: If
Then
Because

6. Vocabulary Introduction
Theory
Definition
Examples
A time-tested concept that makes predictions about the natural world. Once proposed, it must be tested over again. It may be thrown out or modified.

7. Vocabulary Introduction
Law
Definition
Examples
If a theory survives many tests it becomes a law. It summarizes observed experimental facts.

8. Steps of Scientific Inquiry
Uses senses to make observations.
Makes inferences or predictions based on observations.
Research the topic
Form a hypothesis
Design a controlled experiment to test the hypothesis
Perform the experiment and record data
Draw a conclusion
Hypothesis is Accepted
Hypothesis is Rejected
Becomes a Theory
Go back and redesign your hypothesis
Accepted many times and proven mathematically
Becomes a Law

9. A Controlled Experiment Has…
Experimental Group
Same as the Control Group, but with the variable
Manipulated Variable
The one difference between the control and experimental group
Control Group
Setup according to “normal” conditions
Important Points:
They are exactly the same except for the experimental group having the variable(the one difference)
The larger the sample size, the more accurate the results

10. Independent Variable Dependent Variable
The manipulated/experimental variable
This variable is the one you manipulate
What you the scientist can change
Dependent
Variable
The responding variable
This is what you measure in the experiment
This variable’s value depends on the independent variable. It shows the results of your manipulation

11. Hypothesis Formation If
The conditions you are setting up (control group vs. experimental group)
Then
Your predicted results. Includes the dependent variable.
(what you think will happen)
(what your measured results will be)
Because
Your explanation for your predicted results. (why)

12. Data Tables To Properly Create a Data Table
Title
The title must describe what is being done. It must be in the following form.
The Relationship Between the
Independent Variable and the Dependent Variable
Example from worksheet
The Relationship between the

13. Data Tables To Properly Create a Data Table
Title
The title must describe what is being done. It must be in the following form.
The Relationship Between the
Independent Variable and the Dependent Variable
Example from worksheet
The Relationship between the temperature of the lunch and

14. Data Tables To Properly Create a Data Table
Title
The title must describe what is being done. It must be in the following form.
The Relationship Between the
Independent Variable and the Dependent Variable
Example from worksheet
The Relationship between the temperature of the lunch and how high you can jump.

15. Data Tables To Properly Create a Data Table
Columns & Rows:
Determine the number of rows and columns
First row is for labels and units
1st Column  Independent Variable
2nd Column  Dependent Variable
Cold Lunch (15OC)
20cm
Hot Lunch
(70OC)
15cm
* Sometimes the first column is the subjects name.
* Sometimes the Independent variable is a comparison (boys height vs. girls height) and you should make a separate column for both. In this case you would compare the average of the two categories.

16. Data Tables To Properly Create a Data Table
Columns & Rows:
Determine the number of rows and columns
First row is for labels and units
1st Column  Independent Variable
2nd Column  Dependent Variable
Temperature of the lunch
Cold Lunch (15OC)
20cm
Hot Lunch
(70OC)
15cm
* Sometimes the first column is the subjects name.
* Sometimes the Independent variable is a comparison (boys height vs. girls height) and you should make a separate column for both. In this case you would compare the average of the two categories.

17. Data Tables To Properly Create a Data Table
Columns & Rows:
Determine the number of rows and columns
First row is for labels and units
1st Column  Independent Variable
2nd Column  Dependent Variable
Temperature of the lunch
Height you can jump
Cold Lunch (15OC)
20cm
Hot Lunch
(70OC)
15cm
* Sometimes the first column is the subjects name.
* Sometimes the Independent variable is a comparison (boys height vs. girls height) and you should make a separate column for both. In this case you would compare the average of the two categories.

18. Data Tables To Properly Create a Data Table
Columns & Rows:
Determine the number of rows and columns
First row is for labels and units
1st Column  Independent Variable
2nd Column  Dependent Variable
Temperature of the lunch
(OC)
Height you can jump
Cold Lunch (15OC)
20cm
Hot Lunch
(70OC)
15cm
* Sometimes the first column is the subjects name.
* Sometimes the Independent variable is a comparison (boys height vs. girls height) and you should make a separate column for both. In this case you would compare the average of the two categories.

19. Data Tables To Properly Create a Data Table
Columns & Rows:
Determine the number of rows and columns
First row is for labels and units
1st Column  Independent Variable
2nd Column  Dependent Variable
Temperature of the lunch
(OC)
Height you can jump
(cm)
Cold Lunch (15OC)
20cm
Hot Lunch
(70OC)
15cm
* Sometimes the first column is the subjects name.
* Sometimes the Independent variable is a comparison (boys height vs. girls height) and you should make a separate column for both. In this case you would compare the average of the two categories.

20. Data Tables cont.. To Properly Create a Data Table
Labels
Provide the label/heading (name, height, etc…) for each column in the first row.
Units
Provide the appropriate units (cm, feet, seconds, etc…) for the first row labels.
Sort Data
Place in an order, either least to greatest or greatest to least.

21. The Relationship Between
________________ and ______________

22. The Relationship Between
A teacher’s hair color and ______________

23. The Relationship Between
A teacher’s hair color and how fast they run

24. The Relationship Between
A teacher’s hair color and how fast they run
Teachers Hair

25. The Relationship Between
A teacher’s hair color and how fast they run
Teachers Hair
Speed

26. The Relationship Between
A teacher’s hair color and how fast they run
Teachers Hair (color)
Speed

27. The Relationship Between
A teacher’s hair color and how fast they run
Teachers Hair (color)
Speed (minutes/mile)

28. The Relationship Between
A teacher’s hair color and how fast they run
Teachers Hair (color)
Speed (minutes/mile)
Brown
Blonde
Grey
Black
Red

29. The Relationship Between
A teacher’s hair color and how fast they run
Teachers Hair (color)
Speed (minutes/mile)
Brown
8:42
Blonde
9:22
Grey
7:52
Black
8:13
Red
9:05

30. The Relationship Between
A teacher’s hair color and how fast they run
Teachers Hair (color)
Speed (minutes/mile)
Grey
7:52
Black
8:13
Brown
8:42
Red
9:05
Blonde
9:22

31. The Relationship between ___________ and _____________

32. The Relationship between Pam cooking oil and _____________

33. The Relationship between Pam cooking oil and the speed of a sled

34. The Relationship between Pam cooking oil and the speed of a sled
Hole

35. The Relationship between Pam cooking oil and the speed of a sled
Hole
Sled without cooking oil time

36. The Relationship between Pam cooking oil and the speed of a sled
Hole
Sled without cooking oil time
Sled with cooking oil time

37. The Relationship between Pam cooking oil and the speed of a sled
Hole
(#)
Sled without cooking oil time
Sled with cooking oil time

38. The Relationship between Pam cooking oil and the speed of a sled
Hole
(#)
Sled without cooking oil time (seconds)
Sled with cooking oil time

39. The Relationship between Pam cooking oil and the speed of a sled
Hole
(#)
Sled without cooking oil time (seconds)
Sled with cooking oil time (seconds)

40. The Relationship between Pam cooking oil and the speed of a sled
Hole
(#)
Sled without cooking oil time (seconds)
Sled with cooking oil time (seconds)
First Hole
Second Hole
Third Hole
Fourth Hole
Fifth Hole
Sixth Hole
Seventh Hole
Eight Hole
Ninth Hole

41. The Relationship between Pam cooking oil and the speed of a sled
Hole
(#)
Sled without cooking oil time (seconds)
Sled with cooking oil time (seconds)
First Hole 57
Second Hole 44
Third Hole 36
Fourth Hole 49
Fifth Hole 50
Sixth Hole 54
Seventh Hole 32
Eight Hole 61
Ninth Hole 41

42. The Relationship between Pam cooking oil and the speed of a sled
Hole
(#)
Sled without cooking oil time (seconds)
Sled with cooking oil time (seconds)
First Hole 57 70
Second Hole 44
Third Hole 36 29
Fourth Hole 49 54
Fifth Hole 50 63
Sixth Hole 68
Seventh Hole 32 19
Eight Hole 61 78
Ninth Hole 41 39

43. Constructing a Graph
Graphs and charts are great because they communicate information visually. Graphs are often used in newspapers, magazines and businesses around the world.

44. Constructing a Graph cont..
Line Graph vs. Bar Graph
Bar Graphs
Used to graph information that is not continuous.
Used to compare specific data of different items.
Example: Boys compared to Girls
Color compared to other colors
Line Graphs
Used to show information that is continuous
Example: Temperature, Time, etc…

45. Types of Graphs Histograms The bars are touching each other
Each bar represents an interval or range
(5 – 10, 10 – 15, etc…)

46. Constructing a Graph Title Axis Labels and Units
The Relationship Between the Independent and the Dependent Variable
Axis Labels and Units
The independent variable goes on the x-axis (horizontal) and the dependent goes on the y-axis (vertical
Dependent
Variable
Independent
Variable

47. Constructing a Graph… Title:
Relationship between the independent variable and the dependent variable.
Axis:
Independent Variable goes on the x-axis (horizontal)
Dependent Variable goes on the y-axis (vertical)
Labels & Units:
Each axis must have a label and include the units in which you are measuring.

48. Constructing a Graph… Scaling: Numbering the Grid How to Scale A Graph
Count the Spaces in each axis
Divide the upper range of data by the number of spaces to get equal intervals on each line.

49. Line Graphs
Line Graphs are used to compare things when the data represents a continuous process. Example… We measured Mr. Slotoroff every 3 years of his life since birth

50. Analyzing a Line Graph
Although we did not measure Mr. Slotoroff after 20 tons of chocolate, we can still determine his approximate height at that point. This is called Interpolation.
Interpolation
Using the graph to determine values between 2 points of data.

51. Analyzing a Line Graph
We can also figure out how tall he will be after 100 tons of chocolate. We can extend the line graph, which is called Extrapolation.
Extrapolation
Using the graph to determine values beyond the graph.

52. Trend Line
The points on a line graph can be connected with a line because they represent a continuous relationship. When connecting the dots you are assuming that what happens between the dots is the same pattern as the dots themselves
The dots make a pattern or trend. Scientists look at the trend of data, not the individual data points. To better represent the trend of data, they draw a trend line. This line is a better representation of the trend in the data than you would get connecting the dots

53. Old

54. Graphing Example
I think the amount of rain has an effect on the production of corn for high fructose corn syrup (HFS) in all your overly sweetened carbonated and non-carbonated drinks. My results are as follows
2002 we had 7cm of rain and produced 45 bushels
2003 we had 15cm of rain and produced 60 bushels
2004 we had 30cm of rain and produced 20 bushels
2005 we had 11cm of rain and produced 50 bushels
2006 we had 26cm of rain and produced 48 bushels
2007 we had 18cm of rain and produced 65 bushels
2008 we had 20cm of rain and produced 78 bushels
2009 we had 24cm of rain and produced 60 bushels