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Statistics > Quiz 1 > Flashcards

Quiz 1 Flashcards

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
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2
Q

Statistic

A

-Quantity calculated from a sample

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3
Q

Population

A

-Set of all objects that we’re interested in researching

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4
Q

Parameter

A

-Quantity calculated from a population

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5
Q

Significance

A

-Unlikely to have occurred by chance alone

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6
Q

Sample

A

-Subset of a population

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7
Q

Random Sample

A

-Each member of a population has equal likelihood of being chosen

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8
Q

X-bar
_
X

A

-Sample mean

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9
Q

S^2

A

-Sample variance

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10
Q

S

A

-Sample standard deviation

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11
Q

U (mew)

A

-Population mean

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12
Q

O^2 (sigma squared)

A

-Population variance

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13
Q

O (sigma)

A

-Population standard deviation

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14
Q

Descriptive Statistics

A

-Numbers that summarize or describing data

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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

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16
Q

Measures of Central Tendency

A
  • Mean: arithmetic average
  • Median: middlemost score
  • Mode: most frequently occurring score
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17
Q

Measures of Dispersion

A
  • Range
  • Standard Deviation
  • Variance
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18
Q

Range

A

-Largest score - Smallest score

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19
Q

Variance

A

-Average of square deviation about (from) the mean

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20
Q

Standard Deviation

A

-Square root of variance

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21
Q

Types of Frequency Distributions

A
  • Leptokurtosis
  • Platykurtosis
  • Normality
  • Skew
  • Kurtosis
  • Bimodal
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22
Q

Kurtosis

A

-The peakedness or flatness around the mode of a frequency distribution

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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
Q

Normality (Normal Distribution)

A
  • The left and right sides look alike (if you split the graph down the middle)
  • a.k.a. Bell curve or Gaussian distribution
26
Q

Skew

A

-Refers to the amount of asymmetry of the distribution

27
Q

Positively Skewed

A

-Vast majority of the data is on the left (or low side) of the tail

28
Q

Negatively Skewed

A

-Vast majority of the data is on the right (or high side) and the tail is pointing to the left

29
Q

Frequency Polygon

A

-Shows the distribution of subjects scoring among various intervals

30
Q

Histogram

A

-A graphical representation of the data using bars

31
Q

Bar Chart

A

-When the x-axis is categorical in nature

32
Q

Bimodal Distributions

A
  • Most common distribution found in psychology

- Two humps

33
Q

Sampling Distribution

A

-A distribution under repeated sampling and equal-sized samples of any statistic

34
Q

Z-Scores

A
  • 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
Q

Characteristics of standard normal distribution

A
  • Has a mean (or u) or 0

- Has standard deviation (or o (theta)) of 1

36
Q

Confidence Interval

A

-A 95%, 99% (or some stated) probability that the interval falls around (or about) the parameter (u)

37
Q

df

A
  • Degree of freedom (independent piece of information)
  • Can change, but for confidence interval, it will be N-1
  • If N>/= 100, use infinity
38
Q

T-Distribution

A

-William Gosset

  • Is leptokurtic, and as you increase the sample size, it becomes normal, such that t(sub infinity)=z
    • Kate Moss
39
Q

Standard Error

A

-Standard deviation of a sampling distribution

40
Q

Hypothesis

A

-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
Q

Independent Variable

A

-A variable that is manipulated by the experimenter

42
Q

Dependent Variable

A
  • A variable that is measured

- A score

43
Q

Null Hypothesis

A
  • 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
Q

Type I Error

A

-Probability of rejecting the null hypothesis when in fact it’s true

45
Q

Type II Error

A
  • Inability of detecting a difference if in fact one exists

- Insensitivity of experiment

46
Q

Power

A
  • Ability to detect a difference if in fact one exists

- Sensitivity of the experimentsp

47
Q

Determinants of Alpha

A
  • (fishy looking thing)
    1. Experimenter
    1. Journal Editors
  • We usually set our alpha = .05
48
Q

Determinants of Power

A
  • Power and B go opposite
  • Look in notes
    1. N goes up, Power goes up, B goes down
    1. O^2 goes up, Power goes down, B goes up
    1. Skew goes up, Power goes down, B goes up
    1. Outliers go up, Power goes down, B goes up
    1. Difference between group means go up, Power goes up, B goes down
    1. Alpha goes up, Power goes up, B goes down
    1. One vs. Two-Tailed Tests: 1 tail (more power); 2 tail (less power)
49
Q

Heuristic Formula of F (in words)

A

of folks in a group x(times) variance among group members/ average variance within groups

50
Q

Is the F-Ratio that you compute larger than the critical value in the table? (Conclusion).

A
  • 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
Q

Assumptions of ANOVA. Why Fisher made them?

A
    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
  1. 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
Q

What happens when you violate the assumptions?

A

-1. Robust = when you violate the assumption, the Type I error rate doesn’t change appreciably from the normal level. (stated)

    1. Liberal = when you violate the assumption, the Type I error rate is higher than the nominal level
      - We don’t like these tests
  1. 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
Q

Characteristics of F

A
    1. F distribution is positively skewed
    1. X-bar(sub F) = df(sub w)/df(sub w) -2
    1. F (sub infinity), infinity = always 1.00
      - Any ration below 1.00 will be nonsignificant
54
Q

Correlation Factor

A

-Takes the raw data and converts it into deviational scores

55
Q

Is the t value that you compute higher than the critical value in the table? (Determinants of Alpha).

A

LOOK IN PACKET

56
Q

1-Alpha (goes after type 1 error)

A

Probability of not rejecting the null hypothesis when in fact it’s true

Statistics (3 decks)

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