1. Vocab Quiz—History, Research Methods
Turn in to the inbox—Correlations handout
Pick up scantron & testing folders.
fill in scantron—PEN ONLY
Create testing nook or move desk accordingly.
Complete quiz (do NOT write on test packet) and return to front desk.

2. Lets learn more about statistical significance
Welcome!
Lets learn more about statistical significance
Have out on your desk 2C for a date stamp

3. How Psychologists Ask and Answer Questions Statistics

4. What you need to know by the end of today:
Distinguish the purposes of descriptive statistics and inferential statistics.

5. Statistical Reasoning
Statistical procedures analyze and interpret data and let us see what the unaided eye misses.
Composition of ethnicity in urban locales

6. Descriptive Statistics
Describe sets of data
Frequency polygon/histogram
Measures Central Tendency
Mean, Median, Mode
Measures Variability
Range, Standard Deviation (z score)

7. Describing Data
Meaningful description of data is important in research. Misrepresentation can lead to incorrect conclusions.

8. Measures of Central Tendency
Mean: The arithmetic average of scores in a distribution obtained by adding the scores and then dividing by their number.
Median: The middle score in a rank-ordered distribution
Mode: The most frequently occurring score in a distribution.

9. Is MEAN the best measurement of Central Tendency
Is MEAN the best measurement of Central Tendency? A lot of people think so.
Let’s say our class’ average income is 20K a year. But you only made 2K this year—does that mean represent you?
No, its not the best measure for us.
The outliers can skew our data.

10. Measures of Variation
Range: The difference between the highest and lowest scores in a distribution.
Standard Deviation: average difference to the mean (ex. Test score average was 75%. You get 45%. Difference between your score and the mean? 30 points, but what’s the average difference?)
Large SD = more spread out scores are from mean
Small SD = more scores bunch together around the mean
OBJECTIVE 3-14| Explain two measures of variation.

11. Standard Deviation You don’t NEED to know how to calculate, but how to apply the concept
For example…which of the following sets of data have the GREATEST SD?
1, 5, 7, 30
5, 10, 12, 18
30, 32, 34, 35
How do you figure this out????
Can estimate SD by looking at “spread” of #s
Can find mean and compare each # to the mean

12. Calculating Standard Deviation
Put scores in descending order (x)
Find mean of scores (x)
Subtract mean from every score ( x-x)
Square the answer of #3 for each score (x-x)2
Calculate standard deviation…SD
                                                                            SD = standard deviation
SD = √∑ (x-x)2                ∑ = sum of           x = scores
            n – 1                  x = mean              n = # of scores

13. Calculating Standard Deviation
SCORES… 8, 7, 8, 5, 3, 6, 4, 8, 6, 5
SD = √∑ (x-x)2
            n – 1 X X X X
(X - X)
(X - X)
(X - X)2
(X - X)2 8 6 2 4 8 6 2 4
SD = √∑ (28)
            10 – 1 8 6 2 4 7 6 1 1 6 6
SD = √ 28
              9 6 6 5 6 -1 1 5 6 -1 1
SD = √ 3.11 4 6 -2 4
SD = 1.76 3 6 -3 9

14. Standard Deviation
Normal distribution = a distribution of scores that produces a bell-shaped symmetrical curve
In this “normal curve” the mean, median and mode fall at exactly the same point
The span of ONE SD on either side of the mean covers approximately 68.2% of the scores in a normal distribution
OBJECTIVE 3-1
Average IQ = 100
Most (68.2%) people fall into range
IQ extremes are above 130 and below 70

15. Normal Curve  50 % 50 %  1 SD from the mean = 68.27%
Mean, median and mode
 50 %
50 % 
1 SD from the mean = 68.27%
2 SD from the mean = 95.45%
3 SD from the mean = 99.73%
4 SD from the mean = %.

16. Measures of Central Tendency If mean, median and mode differ—the distributions are skewed. Not symmetrical
A Skewed Distribution
Why is this distribution skewed?
How would it change if you removed the families that made 90, 475 and 710?

17. Skewed Distributions ?? Negative vs. Positive
Majority of scores above the mean…one or a few extremely LOW scores cause the mean to be less than the median score
Majority of scores below mean…one or a few extremely HIGH scores cause mean to be greater than median score

18. Example of Skews w/wages
NEGATIVE
AP exam—most students got A/B grades, but one student got an F, the distribution of scores skewed on the lower end.
POSITVE
Most of you make $12.00/hr but one friend make $18.00/hr
Distribution of wages are skewed to friend

19. Inferential Statistics
involves estimating what is happening in a sample population for the purpose of making decisions about that population’s characteristics (based in probability theory)
Basically, inferential statistics allow us to say…”if it worked for this population, we can estimate that it will work with the rest of the population”
i.e. drug testing – if the meds worked for the sample, we estimate they will have the same effects on the rest of the population
There is always a chance for error in whatever the findings may be, so the hypothesis and results must be tested for significance

20. Inferential Statistics

21. Inferential Statistics
Null Hypothesis – states that there is NO difference between two sets of data
Like the opposite of your actual hypothesis.
Purpose of null hypothesis…
until the research SHOWS (by proving the original/alternative hypothesis) that there is a difference, the researcher must assume that any difference present is due to chance
Cant say, “I have great research!” Not until its proven its not due to chance. Have to use statistical significance.

22. Null Hypothesis
Truth About Population
NULL TRUE
NULL FALSE
REJECT NULL
(accept original)
Type I Error
Correct decision
Decision Researcher Makes
ACCEPT NULL
Correct decision
Type II Error
Type I Error: Reject the null (choosing the original hypothesis), yet the null is actually true
Type II Error: Accept the null, yet the original hypothesis is actually correct

23. Evaluating Results INFERENTIAL STATISTICS:
Are results due to chance or b/c of my manipulation of IV?
Can results be applied to larger population from which sample was collected?
Ex: Does caffeine affect reaction time?  
Null hypothesis – It does not.
Control: Decaf
Experimental: Dunkin
IV – Type of coffee drinking
DV – Reaction speed (ruler drop) measured in inches
Take data, do a t-test, find p value .037
P-Value = .05 for statistical significance.  
5% likely the results are due to chance.
95% sure our results are statistically significant!

24. Null Hypothesis
Original hypothesis - “A bomb threat was called into the front office, so we need to evacuate the school.”
Null Hypothesis – “There is no bomb in the school, so we do not need to evacuate.”
Truth About Population
NULL TRUE
NULL FALSE
REJECT NULL
Decision Researcher Makes
ACCEPT NULL

25. Null Hypothesis
Original hypothesis - “A bomb threat was called into the front office, so we need to evacuate the school.”
Null Hypothesis – “There is no bomb in the school, so we do not need to evacuate.”
Truth About Population
NULL TRUE
NULL FALSE
REJECT NULL
Type I Error
Students evacuated, yet bomb squad does not find a bomb
Erred on the side of caution
Correct decision Students evacuated, bomb squad finds bomb & safely removes it…all are safe
Decision Researcher Makes
ACCEPT NULL
Correct decision
no evacuation, no bomb threat ignored, students stay in class & all are safe
Type II Error
Bomb threat is ignored, students stay in class, bomb goes off & students injured

26. Your task:
Complete the HP stats activity (can be worked on as a group, or individually. You and your family decide!) Note: try out the SD question—mostly use it for understanding, and to conclude info for graph.
Done early? Hang on to until next class!!
Complete Reading Guide 2D
Next class =
ethics
Independent/Dependent variable practice
Review quiz weaknesses