1. Experimental Method Looking to prove causal relationships.
Cause and Effect.
Laboratory vs. Field Experiments.
Smoking causes health issues.

2. Steps in Designing an Experiment
Develop a Hypothesis.
Pick Population: Random Selection then Random Assignment.
Operationalize the Variables.
Identify Independent and Dependent Variables.
Look for Extraneous Variables.
Type of Experiment: Blind, Double Blind etc..
Gather Data.
Use statistical analysis to discover whether the difference in the DV between the two groups has been caused by the IV.

3. Terminology for Experiments

4. Hypothesis Expresses a relationship between two variables.
A variable is anything that can vary among participants in a study.
A hypothesis is a testable prediction such as: “Participating in class leads to better grades than not participating”.

5. Operational Definitions
Explain what you mean in your hypothesis.
A specification of the exact procedures used to measure a variable.
How you operationalize the variables will tell us if the study is valid and reliable.
Let’s say your hypothesis is that “chocolate causes violent behavior.”
What do you mean by chocolate?
What do you mean by violent behavior?

6. Independent Variable
Whatever is being manipulated in the experiment by the researcher.
Hopefully the independent variable brings about change.
If there is a drug in an experiment, the drug is almost always the independent variable.

7. Dependent Variable Factor that is being measured in the experiment.
It is dependent on the independent variable.
The dependent variable would be the effect of the drug.

8. Sampling Identify the population you want to study.
The sample must be representative of the population you want to study.
GET A RANDOM SAMPLE.

9. Random Assignment
Once you have a random sample, randomly assigning them into two groups helps control for confounding variables.
Experimental Group: (Group exposed to IV) vs. Control Group: (Group NOT exposed to IV).
Placebo: substance that has no effect apart from a person’s belief in it.
power drink placebo effect
The placebo effect is like a self-fulfilling prophecy. People believe that the placebo will work so it does.

10. Beware of Confounding Variables
The object of an experiment is to prove that A (IV) causes B (DV).
A confounding variable is anything that could cause change in B, that is not A.
If I wanted to prove that smoking causes heart issues, what are some confounding variables?
Lifestyle and family history may also effect the heart.

11. Experimenter Bias A type of confounding variable. Not a conscious act.
To eliminate bias they do…
Single blind studies – participants don’t know what group they’re in.
Double blind studies – participants and researchers don’t know which group is which.
Milgram was a single blind study because the experimenter knew who was being studied but the participants did not know what group they were in.

12. Replication
One other safeguard in the experimental process is to see if you can repeat an experiment and see if the results can be reliably reproduced (replication).

13. Data Analysis
In an experiment, the results are called statistically significant if they are unlikely to have occurred by chance (no more than a 5% chance).

14. APA Ethical Guidelines for Research
IRB - Internal Review Board.
Both for humans and animals.

15. Animal Research Clear purpose. Treated in a humane way.
Acquire animals legally.
Least amount of suffering possible.

16. Human Research Must be voluntary. Informed consent. Confidentiality.
No significant risk of harm or discomfort.
Must debrief the participants afterwards.
Milgram

17. Statistics Recording the results from our studies.
Statistics help to make data meaningful.
Must use a common language so we all know what we are talking about.
Stats are used in psychology to help draw conclusions but can be misused and manipulated.

18. Descriptive Statistics
Just describes sets of data.
One example would be a frequency distribution.
Or even frequency polygons or histograms.

19. Frequency Distributions
A list of scores from highest to lowest.
Helps make the raw data more meaningful.

20. Bar Graphs
Putting the info into a bar graph makes that info even more meaningful because we can see how the scores cluster.

21. Measures of Central Tendency
Mean (the average), Median (middle score) and Mode (most frequent).
Watch out for extreme scores or outliers; this effects the mean most!!
Let’s look at the salaries of the employees at Dunder Mifflen Paper in Scranton:
$25,000-Pam
$25,000- Kevin
$25,000- Angela
$100,000- Andy
$100,000- Dwight
$200,000- Jim
$300,000- Michael
The median salary looks good at $100,000.
The mean salary also looks good at about $110,000.
But the mode salary is only $25,000.
Maybe not the best place to work.
Then again living in Scranton is kind of inexpensive.

22. Normal Distribution
In a normal distribution, the mean, median and mode are all the same.

23. Normal Distribution
68% of the population fall within one standard deviation of the average.
96% fall within two SD’s of the average.

24. Distributions Outliers lead to skewed distributions.
If a group has one high score, the curve has a positive skew (contains more low scores).
If a group has a low outlier, the curve has a negative skew (contains more high scores).

25. A Skewed Distribution
Are the results positively or negatively skewed? Look at how much the mean is affected!

26. Other measures of variability
Range: distance from highest to lowest scores.
Standard Deviation: the variance of scores around the mean.
The higher the variance or SD, the more spread out the distribution is.
Do scientists want a big or small SD?
A small one... less variance.
Kobe and LeBron may both score 30 ppg (same mean).
But their SDs are very different.

27. Comparative Statistics
Two major types:
Percentage (compares to a perfect score of 100).
Percentile rank (compares to other scores of an imaginary 100 people).

28. Correlation Coefficient
A number that measures the strength of a relationship.
Range is from -1 to +1
The relationship gets weaker the closer you get to zero.
Which is a stronger correlation?
-.13 or +.38
-.72 or +.59
-.91 or +.04

30. How to Read a Correlation Coefficient

31. Inferential Statistics
The purpose is to discover whether the findings can be applied to the larger population from which the sample was collected.
Remember “statistical significance.” (Is it unlikely the results occurred by chance??

32. Statistical Significance
When two distributions show little overlap, the difference between them is LESS likely due to chance... Therefore, statistically significant.