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Psychology 312 > Part 3 > Flashcards

Part 3 Flashcards

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1
Q

inferential statistics

A

making inferences from distributions

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2
Q

the normal distribution

A

describes a common probability distribution of values of a continuous variable around the mean

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3
Q

vertical axis=

A

frequency of values

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4
Q

horizontal axis=

A

continuous values (scores total)

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5
Q

properties of the normal distribution:

A

symmetric, unimodal, mean mode and median are the same value and are equal to the centre of distribution

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6
Q

the specific shape of the curve depends on :

A

mean (location of peak on x-axis), standard deviation (how spread out the curve is)

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7
Q

standardization of normal distribution

A

allows comparison of relative standing for different scales, standardize by converting raw score deviations into standard deviation units (z-scores)

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8
Q

properties of standard normal distribution

A
  1. the cumulative area (read left to right) is close to zero for z-scores close to -3.49
  2. the cumulative area increases as the z-scores increases
  3. the cumulative area for z=0 is 0.50
  4. the cumulative area is close to 1 for close to z=3.49
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9
Q

z-score correlates directly to:

A

standard of deviation from the mean

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10
Q

properties of probability with continuous variables:

A

limitless number of possible values for this variable, probability of event falling in an interval, described as the area under the curve for a specified interval

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11
Q

properties of probability with discrete variables:

A

can take on limited number of possible values, probability of specific outcomes

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12
Q

importance of probability in everyday life:

A
  • probability judgments
  • knowledge of probability helps us understand human limitations
  • consumer judgment
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13
Q

what are the types of probabilities?

A

subjective probability, analytic or theoretical probability, expected relative frequency (empirical) probability

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14
Q

probability experiment

A

an action, or trial, through which specific results are obtained

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15
Q

outcome

A

the result of a single trial in a probability experiment

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16
Q

sample space

A

the set of all possible outcomes of a probability

17
Q

event

A

consists of one or more outcomes and is a subset of the sample space

18
Q

range of probabilities rule

A

the probability of an event E is between 0 and 1

19
Q

subject probability

A

personal judgment of event likelihood

20
Q

analytic or theoretical probability

A

determines probability of specific outcomes by looking at all possible outcomes (P(E)=number of outcomes in event E/number of outcomes in sample space)

21
Q

expected relative frequency (empirical) probability

A

probability in the long run or on average (apply law of large numbers)

22
Q

law of large numbers

A

as an experiment is repeated over and over, the empirical probability of an event approaches the theoretical (actual) probability of the event

23
Q

sample with replacement

A

sampling in which the item drawn on trial is replaced before drawing trial N+1

24
Q

sample without replacement

A

sampling in which the item drawn on trial is not replaced before drawing trial N+1

25
Q

what are the types of events

A

independent events, (dependent events), mutually exclusive events

26
Q

independent events

A

occurrence of one event has no effect on the occurrence of another

27
Q

opposite of independent events are:

A

dependent events (occurence of one event has an effect on the occurrence of another)

28
Q

mutually exclusive events

A

when the occurrence of one event precludes the occurrence of another, i.e., two events cannot occur at the same time

29
Q

if two events can occur at the same time, then the event is:

A

not mutually exclusive

30
Q

given a set of mutually exclusive events…

A

the probability of the occurrence of one event or another is equal to the sum of their separate probabilities (ADDITION RULE)

31
Q

to find the probability of the co-occurrence of two or more independent events:

A

calculate JOINT PROBABILITIES

32
Q

probability of the joint occurrence of two INDEPENDENT events is….

A

the product of their probabilities (MULTIPLICATION RULE)

33
Q

conditional probability

A

the probability of an event occurring, given that some other event has already occurred

34
Q

denotation of conditional probability

A

P(B | A) = probability of B, given A

Psychology 312 (10 decks)

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