# Psych 5 Final

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1. Hypothesis testing compares
Known population before treatment and unknown population after treatment
2. Hypothesis testing is usually used in what type of study
research
3. Hypothesis testing tests the ___ of the treatment
effectiveness
4. Hypothesis testing compares __ to eachother
means

• compare sample mean to population mean
• or compare two or more sample means to eachother
5. Differences between M and u are expected by __
This is called __ __ and __ __
• chance
• sampling variability
• sampling error
6. Goal of hypothesis testing is to rule out __ chance as a plausible explanation for the results of a study
chance
7. Hypothesis Testing
Statistical method using sample data to evaluate a hypothesis about a population
8. Difference is statistically significant when

-
-
• -M is unlikely to have occurred by chance
• -M is in the extreme tails of the sampling distribution
9. "Unlikely" or "Extreme" percentage
• -If M would occur 5% or less of the time
• -p<0.05
10. We always assume _ difference between the means, called the __ __
no difference between the means, called the null hypothesis
11. Null hypothesis
-If true, M should be close to _
-If M is not close to _, we reject
• -u
• -if M is not close to u, we reject the null hypothesis
12. The type of statistics that deals with hypothesis testing
inferential statistics
13. Hypothesis Testing Steps

-
-
-
-
• -State the hypothesis
• -Set the criterion for a decision
• -Compute the test statistic
• -Make a decision
14. Step 1:

-
-
-Ho: Predicts there is no change, difference, or relationship between our treatment and the general population

-H1: There is a change in the population after we apply the treatment
15. H0 and H1 are __ __
mutually exclusive (if one is true, the other cannot be)
16. Non-directional hypothesis
• -Also known as a two-tailed hypothesis
• -Direction of difference is not specified
17. Step 2:
Create a decision rule, find the critical value, put probability, one or two-tailed
18. Critical Value
boundary between likely/unlikely outcomes
19. Critical Region
area under the curve more extreme than the critical value
20. Decision Rule
Reject H0 if observed test statistic falls in the critical region (exceeds critical value)
21. Step 3:
Compute the test statistic

-z score formula
-standard deviation formula
See paper
22. Step 4:
Reject or fail to reject H0
23. When we reject H0

-Difference between means is unlikely to be due to __
-We call this a __ __ finding
-We can never __ or __ H0 or H1
• -chance
• -statistically significant
• -prove or disprove
24. When we fail to reject H0

-Due to __
-We call this __ __
-We don't accept or prove H0, we just don't find evidence supporting _
• -chance
• -statistically significant
• -H1
25. We never __ or __ a difference
-prove or disprove
26. Type 1 error
• -Rejecting a null hypothesis when it's actually true
• -Concluding a treatment has an effect when in reality it doesn't
27. How do we control Type 1 Error?
• -We do this with our alpha level, or p value
• -a=0.05 --> there's a 5% chance we will falsely reject the null hypothesis
• 5% chance of type 1 error
• -a=0.01--> there's a 1% chance we will falsely reject the null hypothesis
28. Type 2 Error
• -Failing to reject a null hypothesis that is false
• -The test failed to detect a real treatment effect
29. B
-probability of making a type II error when Ho is false
30. Minimizing type 2 error is increasing __
Based on __ and __
• -power
• -effect size and sample size
31. Power

-Power is greater when

1)
2)
• -Effect size is bigger because big effects are easy to use
• -Sample size is larger because large samples make it easy to detect tiny effects
32. We increase power (1-B) by

1)
2)
3)
4)
• -Increase sample size (n) is the best possible way
• -Increase treatment effect size
• -Choose less stringent alpha level
• -Use a one-tailed test
33. Uncertainty and Error

-We never know
-We try to minimize
-We balance the risk of
-Three alpha levels researchers use
• -The absolute truth
• -p (making a mistake)
• -Type I and Type II error
• -0.05, 0.01 or 0.001
34. Effect size

Different types of statistics
Descriptive statistic that indicates magnitude of an effect

Cohen's d, r2, eta-squared
35. Cohen's D formula

What does Cohen's D tell us
see paper

-difference between means in standard deviation units
36. Cohen's D

-small effect
-medium effect
-large effect
• -0.2
• -0.5
• -0.8
37. Two-tailed tests (non-directional) have critical regions in
One-tailed tests (directional) have critical region
• -each tail
• -in the lower negative tail only-for one tailed tests, critical value is smaller
38. Assumptions of the z-test
1)
2)
3)
4)
• Distribution of sample means is normal
• Individuals were randomly sampled
• Independent observations
• Know that SD does not change with treatment
39. What do you state when reporting results?

1)
2)
3)
4)
• -Two means
• -Z score
• -probability
• -tails
40. Unimodal Distribution
Distributions with one clear peak
41. Bimodal DIstributions
Distributions with two clear peaks
42. Uniform Distribution
• A probability distribution for which all of the values that a random variable can take on or occur with equal probability
• Or the shape of a graph that has no peaks and valleys
43. Symmetric Distribution
Can be divided at the center so that each half is a mirror image of the other
44. Standard Normal Distribution
• Occurs when a normal random variable has a mean of zero and a standard deviation of one
• The normal random variable is called a z score
45. Skewed right
Distributions with fewer observations on the right
46. Skewed left
Distributions with fewer observations on the left
47. Normal Distribution
• All normal distributions look like a symmetric, bell-shaped curve
• Associates the normal random variable X with a cumulative property
48. Positive Skewness
The tail on the right side is longer or fatter than the left side
49. Negative Skewness
Tail on the left side of the distribution is longer or fatter than the left side
50. Long tail of a distribution
Portion of the distribution having a large number of occurrences far from the "head" or central part of the distribution
51. Body of a distribution
Middle of the bell curve
52. 1-B
• -Probability of making a correct decision when His false
• -Ability to detect a real treatment effect
 Author: Trekofstarsx ID: 298286 Card Set: Psych 5 Final Updated: 2015-03-15 19:57:25 Tags: Psych Folders: Description: Psych 5 Final Review Show Answers: