Quiz 1 Flashcards
(56 cards)
1
Q
State Central Limit Theorem
A
- CLT
- Given a population with finite mean u (mew) and finite variance O^2 (sigma squared), the sampling distribution of the mean approaches a normal distribution with mean u and variance O^2/N, as N, the sampling size, increases
2
Q
Statistic
A
-Quantity calculated from a sample
3
Q
Population
A
-Set of all objects that we’re interested in researching
4
Q
Parameter
A
-Quantity calculated from a population
5
Q
Significance
A
-Unlikely to have occurred by chance alone
6
Q
Sample
A
-Subset of a population
7
Q
Random Sample
A
-Each member of a population has equal likelihood of being chosen
8
Q
X-bar
_
X
A
-Sample mean
9
Q
S^2
A
-Sample variance
10
Q
S
A
-Sample standard deviation
11
Q
U (mew)
A
-Population mean
12
Q
O^2 (sigma squared)
A
-Population variance
13
Q
O (sigma)
A
-Population standard deviation
14
Q
Descriptive Statistics
A
-Numbers that summarize or describing data
15
Q
Inferential Statistics
A
- More in terms of hypothesis testing
- Allow us to test hypotheses about the differences between groups on the variable being measured
16
Q
Measures of Central Tendency
A
- Mean: arithmetic average
- Median: middlemost score
- Mode: most frequently occurring score
17
Q
Measures of Dispersion
A
- Range
- Standard Deviation
- Variance
18
Q
Range
A
-Largest score - Smallest score
19
Q
Variance
A
-Average of square deviation about (from) the mean
20
Q
Standard Deviation
A
-Square root of variance
21
Q
Types of Frequency Distributions
A
- Leptokurtosis
- Platykurtosis
- Normality
- Skew
- Kurtosis
- Bimodal
22
Q
Kurtosis
A
-The peakedness or flatness around the mode of a frequency distribution
23
Q
Leptokurtosis
A
- More scores in the tails and fewer scores in the middle as compared to the corresponding normal distribution
- Tends to happen more in smaller samples
24
Q
Platykurtosis
A
-Fewer scores in the tails and more scores in the middle as compared to the corresponding normal distribution
25
Normality (Normal Distribution)
- The left and right sides look alike (if you split the graph down the middle)
- a.k.a. Bell curve or Gaussian distribution
26
Skew
-Refers to the amount of asymmetry of the distribution
27
Positively Skewed
-Vast majority of the data is on the left (or low side) of the tail
28
Negatively Skewed
-Vast majority of the data is on the right (or high side) and the tail is pointing to the left
29
Frequency Polygon
-Shows the distribution of subjects scoring among various intervals
30
Histogram
-A graphical representation of the data using bars
31
Bar Chart
-When the x-axis is categorical in nature
32
Bimodal Distributions
- Most common distribution found in psychology
| - Two humps
33
Sampling Distribution
-A distribution under repeated sampling and equal-sized samples of any statistic
34
Z-Scores
- Statistic that allows us to determine how far we are away from the mean
- a.k.a. Standard scores
- Determine how far away scores are from the mean in standard deviational units
- May be both descriptive and inferential
35
Characteristics of standard normal distribution
- Has a mean (or u) or 0
| - Has standard deviation (or o (theta)) of 1
36
Confidence Interval
-A 95%, 99% (or some stated) probability that the interval falls around (or about) the parameter (u)
37
df
- Degree of freedom (independent piece of information)
- Can change, but for confidence interval, it will be N-1
- If N>/= 100, use infinity
38
T-Distribution
-William Gosset
- Is leptokurtic, and as you increase the sample size, it becomes normal, such that t(sub infinity)=z
- Kate Moss
39
Standard Error
-Standard deviation of a sampling distribution
40
Hypothesis
-Educated guess about which group will be significantly higher on a measure or if there will be a positive or negative relationship between two measurements
41
Independent Variable
-A variable that is manipulated by the experimenter
42
Dependent Variable
- A variable that is measured
| - A score
43
Null Hypothesis
- Ho (that’s a sub o)
- ”Null” means “no”
- No difference in rates, scores, etc.
- No statistically significant difference between or among population means on a particular measurement
44
Type I Error
-Probability of rejecting the null hypothesis when in fact it’s true
45
Type II Error
- Inability of detecting a difference if in fact one exists
| - Insensitivity of experiment
46
Power
- Ability to detect a difference if in fact one exists
| - Sensitivity of the experimentsp
47
Determinants of Alpha
- (fishy looking thing)
- 1. Experimenter
- 2. Journal Editors
- We usually set our alpha = .05
48
Determinants of Power
- Power and B go opposite
- Look in notes
- 1. N goes up, Power goes up, B goes down
- 2. O^2 goes up, Power goes down, B goes up
- 3. Skew goes up, Power goes down, B goes up
- 4. Outliers go up, Power goes down, B goes up
- 5. Difference between group means go up, Power goes up, B goes down
- 6. Alpha goes up, Power goes up, B goes down
- 7. One vs. Two-Tailed Tests: 1 tail (more power); 2 tail (less power)
49
Heuristic Formula of F (in words)
of folks in a group x(times) variance among group members/ average variance within groups
50
Is the F-Ratio that you compute larger than the critical value in the table? (Conclusion).
- If yes, statistically significant. Group A is significantly higher than Group B on the dv.
- p < .05; p< .01
- If no, not statistically significant (nonsignificant). There is no statistically significant difference between Group A and Group B on the dv.
- p > .05; n.s.
51
Assumptions of ANOVA. Why Fisher made them?
- 1. Normality in the Population
- a. That’s how Fisher derived out the critical values of F
- b. From CLT, the sampling distribution of the mean approaches normality as N increases
- Kolmogorov-Smirnov
- Shapiro-Wilk’s W=1.0
2. Homogeneity (homoscedasticity) of Variance in the Population
O(sub 1)^2 = O(sub 2)^2 = O(sub 3)^2
-Why? Fisher averaged “like commodities” (sample within variation) to obtain his best estimate of O^2 from O^2/N
52
What happens when you violate the assumptions?
-1. Robust = when you violate the assumption, the Type I error rate doesn’t change appreciably from the normal level. (stated)
- 2. Liberal = when you violate the assumption, the Type I error rate is higher than the nominal level
- We don’t like these tests
3. Conservative = when you violate the assumption, Type I error rate is lower than the stated level
- When you violate these assumptions? It is still robust
53
Characteristics of F
- 1. F distribution is positively skewed
- 2. X-bar(sub F) = df(sub w)/df(sub w) -2
- 3. F (sub infinity), infinity = always 1.00
- Any ration below 1.00 will be nonsignificant
54
Correlation Factor
-Takes the raw data and converts it into deviational scores
55
Is the t value that you compute higher than the critical value in the table? (Determinants of Alpha).
LOOK IN PACKET
56
1-Alpha (goes after type 1 error)
Probability of not rejecting the null hypothesis when in fact it's true
