1. Analysis and Interpretation of Experimental Findings
Use statistical analysis to
Claim IV produced an effect on DV
Rule out the alternative explanation that chance produced any observed effect
Replication
Best way to determine whether findings are reliable
Repeat experiment and see if same results are obtained

2. Establishing External Validity of Experiments
External validity of findings are established when the findings are replicated
Field experiments are a way to increase the external validity of laboratory findings by replicating an experiment in a real-world setting.
Partial replications are common: Research findings generalize when a similar result occurs when slightly different experimental procedures are used in a subsequent experiment.
Psychologists also make use of conceptual replications.
We are more interested in relationships among variables considered at the conceptual level rather than in specific conditions, settings, and participants.
Different operational definitions for concepts may be used in replications to fit the particular population or setting.

3. Analysis of Experimental Designs
Three steps
Check the data
Errors? outliers?
Describe the results
Descriptive statistics such as means, standard deviations, effect size
Confirm what the data reveal
Inferential statistics 3

4. Analysis of Experimental Designs
For example Carnagey and Anderson effects of playing violent video games on aggressive affect, cognition and behavior
I.V. Three versions of video game
Reward condition
Punishment condition
Nonviolent condition
D.V.
Hostile emotions
Aggressive cognition
Aggressive behavior
Procedure
Complete word fragments
Half could be aggressive “K I __ __”
Results
Check for errors
Descriptive statistics

5. Analysis of Experiments
Descriptive Statistics
Mean (central tendency)
Average score on DV, computed for each condition
Not interested in each individual score, but how people responded on average in a condition
Standard deviation (variability)
Average distance of each score from the mean of a group
Not everyone responds the same way to an experimental condition 5

6. Analysis of Experiments
Effect Size: The strength of the relationship between the independent variable and the dependent variable
Cohen’s d: This measure of effect size is
difference between treatment mean and control group mean average population standard deviation (σ)
computed as follows:
do not need to know this equation
M1 – M2 where σ = (n1 - 1) s12 + (n2 – 1) s22
σ N
Reward compared to Nonviolent
d = 0.83
Guidelines for interpreting Cohen’s d are:
Small effect: .20 Medium effect: .50 Large effect: .80

7. Analysis of Experiments
Meta-analysis is used to summarize the effect size of an independent variable or dependent variable across many experiments.
Experiments that investigate a psychological phenomenon are selected for review based on their internal validity and other criteria.
An effect size for each experiment is computed (or several effect sizes, depending on the number of variables).
The effect sizes across all the relevant experiments are combined to determine the average effect size across all experiments.

8. Analysis of Experiments
Confirm what the data reveal
Use inferential statistics to determine whether the IV produced a reliable effect on the DV.
Allow us to rule out whether the findings from our experiment might be simply due to chance (error variation).
Two types of inferential statistics:
Null hypothesis testing
Confidence intervals

9. Inferential Statistics
Null Hypothesis Testing
This statistical procedure is used to determine whether the mean difference between two conditions is greater than what might be expected due to chance or error variation.
We say that the effect of an independent variable on the dependent variable is statistically significant when the probability of the results being due to error variation (chance) is low. p < .05
How do we decide this?

10. Steps for Null Hypothesis Testing
(1) Assume the null hypothesis is true. The population means for groups in the experiment are equal
Example; for the Aggressive cognition
means for reward and nonviolent are equal
(2) Use sample means to estimate population means.
Example
mean reward = .210
mean punishment = .175
mean nonviolent = .157
difference between reward and nonviolent = .54
Is the observed mean difference (.54) greater than what is expected when we assume the null hypothesis is true (zero)?

11. Steps for Null Hypothesis Testing
(3) Compute the appropriate inferential statistic.
t-test: test the difference between two sample means
F-test (ANOVA): test the difference among three or more sample means
(4) Identify the probability associated with the inferential statistic
p value is printed in computer output or can be found in statistical tables

12. Steps for Null Hypothesis Testing
(5) Compare the observed probability with the predetermined level of significance (alpha), which is usually p < .05
If the observed p value is greater than .05, do not reject the null hypothesis of no difference
Conclude IV did not produce a reliable effect
I.V. Three versions of video game
No effect on Aggressive cognition

13. Steps for Null Hypothesis Testing
If the observed p value is less than .05, reject the null hypothesis of no difference.
If the p value is < .05, conclude that the version of video game did have an effect on Aggressive cognition
A statistically significant outcome indicates that the difference between the two means observed in the experiment is larger than would be expected if the null hypothesis were true (i.e., no difference in the population).

14. Use of confidence intervals
Compute confidence interval around sample mean in each condition.
If confidence intervals do not overlap, we gain confidence that the population means for the conditions are different
reward group is between.186 to .234
nonviolent group is between .133 to .181
these confidence intervals do not overlap
So the IV has an effect on DV

15. Confidence Intervals (continued)
If confidence intervals overlap slightly, we are uncertain about the true mean difference.
If intervals overlap such that the mean of one group lies within interval of another group, we conclude the population means do not differ.
reward group is between to .234
punishment group is between to .199
these confidence intervals do overlap
So not sure if the IV has an effect on DV

16. The Role of Probability in Inferential Statistics
Probability is used to predict the type of samples that are likely to be obtained from a population.
Thus, probability establishes a connection between samples and populations.
Inferential statistics rely on this connection when they use sample data as the basis for making conclusions about populations.

17. Figure 6-1 The role of probability in inferential statistics
Figure 6-1 The role of probability in inferential statistics. Probability is used to predict what kind of samples are likely to be obtained from a population. Thus, probability establishes a connection between samples and populations. Inferential statistics rely on this connection when they use sample data as the basis for making conclusions about populations.

18. Probability
For a situation in which several different outcomes are possible, the probability for any specific outcome is defined as a fraction or a proportion of all the possible outcomes.
If the possible outcomes are identified as A, B, C, D, and so on, then
probability of A = number of outcomes classified as A
total number of possible outcomes

19. Probability
Probability is a method for measuring and quantifying the likelihood of obtaining a specific sample from a specific population.
We define probability as a fraction or a proportion.
a ratio comparing the frequency of occurrence for that outcome relative to the total number of possible outcomes.
probability of A = number of outcomes classified as A
total number of possible outcomes
If there are 12 men and 18 women in a class the probability of picking a man would be 12/30.

20. Probability and Sampling
To assure that the definition of probability is accurate, the use of random sampling is necessary.
Random sampling requires that each member of a population has an equal chance of being selected.
Independent random sampling includes the conditions of random sampling and further requires that the probability of being selected remains constant for each selection. Also called sampling with replacement.

21. Probability and Frequency Distributions
The situations in which we are concerned with probability usually involve a population of scores that can be displayed in a frequency distribution graph.
If you think of the graph as representing the entire population, then different portions of the graph represent different portions of the population.
Because probabilities and proportions are equivalent, a particular portion of the graph corresponds to a particular probability.

22. If you take a sample of n=1 from this population
what is the probability of getting a score greater then 4? p(X>4) = 2/10
what is the probability of getting a score less then 5? p(X<5) = 8/10
Figure 6-2 A frequency distribution histogram for a population that consists of N = 10 scores. The shaded part of the figure indicates the portion of the whole population that corresponds to scores greater than X = 4. The shaded portion is two-tenths (p = 2/10) of the whole distribution.

23. Probability and the Normal Distribution
If a vertical line is drawn through a normal distribution, several things occur.
The line divides the distribution into two sections. The larger section is called the body and the smaller section is called the tail.
The exact location of the line can be specified by a z-score.

24. Figure 6-3 The normal distribution
Figure 6-3 The normal distribution. The exact shape of the normal distribution is specified by an equation relating each X value (score) with each Y value (frequency). The equation is ( and e are mathematical constants.) In simpler terms, the normal distribution is symmetrical with a single mode in the middle. The frequency tapers off as you move farther from the middle in either direction.

25. Figure 6-4 The normal distribution following a z-score transformation.

26. Normal Curve with Standard Deviation
| + or - one s.d. |

27. Figure 6-5 The distribution for Example 6.2.
What is the probability of selecting someone from this population with an SAT score greater then 700? Or p(X>700) = ?
Shaded area in figure 6.5 is the proportion of SAT score above 700.
Translate to z scores, z = (X – μ) ∕ σ z = ( )/100 = 2
So we are looking for p(z>2.00) = ?
Go back and look at Figure 6.4, what proportion is greater then z of 2.00?
Only 2.28% of the population so p(z>2.00) = 2.28%.
Figure 6-5 The distribution for Example 6.2.

28. Probability and the Normal Distribution
The unit normal table lists several different proportions corresponding to each z-score location.
Column A of the table lists z-score values.
For each z-score location, columns B and C list the proportions in the body and tail, respectively.
Finally, column D lists the proportion between the mean and the z-score location.
Because probability is equivalent to proportion, the table values can also be used to determine probabilities.

29. Figure 6-6 A portion of the unit normal table, see Appendix B page 647
Figure 6-6 A portion of the unit normal table, see Appendix B page 647. This table lists proportions of the normal distribution corresponding to each z-score value. Column A of the table lists z-scores. Column B lists the proportion in the body of the normal distribution up to the z-score value. Column C lists the proportion of the normal distribution that is located in the tail of the distribution beyond the z-score value. Column D lists the proportion between the mean and the z-score value.

30. Figure 6.7 Proportions of a normal distribution corresponding to z = and z = Find 0.25 in column A in the Unit Normal Table in column B is the body and in column C is the tail +

31. Figure 6-8 The distributions for Example 6.3A–6.3C.
p(z>1.00) = 15.87% Look up z score in column A then find proportion in column C
Example 6.3B
p(z<1.50) = 93.32% Look up z score in column A then find proportion in column B
Example 6.3C
p(z<-0.50) = 30.85% Look up z score in column A then find proportion in column C
Figure 6-8 The distributions for Example 6.3A–6.3C.

32. Figure 6-9 The distributions for Examples 6.4A and 6.4B.
20 %
20 %
Example 6.5A
Find z score for top ten percent of distribution, locate in column C then use the closest z score from column A, z = 1.28
Example 6.5b
Find z scores for the middle 60 % of the distribution. Locate in column D then use the closest z score from column A, z = 0.84 and z =
Figure 6-9 The distributions for Examples 6.4A and 6.4B.