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2 Kinds of Statistics:
1.Descriptive: listing and summarizing data
in a practical and efficient way
2.Inferential: methods used to determine
whether data supports a hypothesis or
whether the results are simply due to
chance
Question: Does television viewing before a
test impact test results?
Hypothesis: The more television a student
watches the night before a test, the lower
the test score.
Research Method: Survey
Questions:
1. How many hours of TV did you watch the
night before the test?
2. How many hours of TV did you watch the
night after the test?
3. What was your grade?
4. How many products did you recognize
during commercials?
5. What is your height in inches?
Survey Results
Hours of TV
Watched Before
0.0
Hours of TV
Watched After
1.5
5
2
71
0.5
0.5
1.0
1.0
1.0
1.5
1.5
1.5
1.5
2.0
2.5
2.5
3.0
4.0
2.5
2.5
2.0
2.5
1.5
3.0
2.5
2.5
3.0
3.0
2.5
3.5
3.0
4.0
10
9
10
8
7
9
8
8
5
5
3
4
0
4
4
6
14
10
9
7
12
9
13
13
17
10
18
20
64
69
60
71
63
70
59
75
68
68
65
72
62
67
*Highest possible score = 10
Grade*
Products
Height
So what????
What are we supposed to do with this stuff?
Descriptive Statistics:
listing and summarizing data in a practical and
efficient way
1. Data Distribution (Frequency)
Data Distribution, Part I. Organize data into a frequency table.
Hours
Frequency of
Before
Frequency of After
Data Distribution, Part I. Organize data into a frequency table.
Hours
Frequency of
Before
Frequency of After
0.0
1
0
0.5
2
0
1.0
3
0
1.5
4
2
2.0
1
1
2.5
2
6
3.0
1
4
3.5
0
1
4.0
1
1
Total
15
15
Data Distribution, Part II. Calculate percentages.
For instance, what percentage of participants watched television
for 2.5 hours of television before the test?
2 participants watched for 2.5 hours
15 participants in all
13%
Frequency
(number of students)
Data Distribution, Part III. Create a frequency graph.
Hours of TV
Descriptive Statistics:
listing and summarizing data in a practical and
efficient way
1. Data Distribution (Frequency)
2. Central Tendency (Middles & Averages)
Central Tendency, Part I: The Mode
Mode: Out of list of data, the score or result that occurs most often.
Central Tendency, Part II: The Median
Median: When results or scores are put in order from least to most, the
median is the middle score or result.
Central Tendency, Part III: The Mean
Mean: The average. When all of the scores are added together and that
number is divided by the total number of scores.
Note: The mean is the balance point of the distribution of data. The mean
reflects all of the scores in a set of data.
Descriptive Statistics:
listing and summarizing data in a practical and
efficient way
1. Data Distribution (Frequency)
2. Central Tendency (Middles & Averages)
3. Measures of Variability (Spread)
Measures of Variability, Part I: The Range
Range is the total number of possible scores or results.
Measures of Variability, Part II: Standard Deviation
Standard Deviation: a measure of variability that describes
an average distance of every score from the mean
The IQ Bell Curve
large standard deviation
small standard deviation
Descriptive Statistics:
listing and summarizing data in a practical and
efficient way
1. Data Distribution (Frequency)
2. Central Tendency (Middles & Averages)
3. Measures of Variability (Spread)
4. Correlation Coefficients (Direction & Strength)
Positive Correlation
As one variable increases, so does the other…
Negative Correlation
As one variable increases, the other decreases…
No Correlation
There is no clear correlation between one variable and the other…
Important:
A correlation only shows that there
is a relationship.
It does not indicate cause and
effect.
Correlation Coefficient: a number that
describes the direction and strength of
the relationship between two variables.
Pearson Correlation Coefficient (r):
+ indicates a positive correlation
- indicates a negative correlation
Pearson correlation coefficients can take any value from -1 to +1
-1 is a very strong negative correlation
+1 is a very strong positive correlation
0 indicates a very weak relationship
Inferential Statistics:
mathematical methods used to help make
generalizations about data and to determine
whether results are due to chance or whether
results support the hypothesis
Are the results due to chance?
OR
Is there a real and significant
relationship between the two
variables?
Only Inferential Statistics can answer these questions.
Researchers calculate measures of statistical significance.
The 5% Rule:
When the probability of the result is less than 5%
(or 1% or whatever the researcher decides), then
the result is statistically significant.
This means that the odds are good that the result is
not due to chance.