Lecture 5 Flashcards

1
Q

Measurement Error

A

Parallax (sight), Calibration, Zero, Damage, Limit of reading of the measurement device

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2
Q

Types of Errors:

A

Gross (human), Systematic (Instrumental, environmental & observational), Random (caused by unknown disturbances)

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3
Q

Significance Level

A

= alpha = error rate = probability of rejecting null hypothesis when it’s true

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4
Q

P-value

A

Probability of obtaining an effect as extreme as the one in the sample data assuming the null hypothesis is true

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5
Q

T1 Error

A

Study reveals a difference when there is no actual difference

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6
Q

T2 Error

A

Study reveals no difference when there actually is one

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7
Q

Bias

A

Factors that operate on a sample that make it unrepresentative of the population –> Large enough samples will eliminate unknown factors that cause bias

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8
Q

Confirmation Bias

A

Finding what you’re looking for

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9
Q

Recording Biases

A

Recall bias, only remember first or last things said

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10
Q

Halo effect

A

Non-experimental variables affect experimental measures

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11
Q

Social Desirability Bias

A

Participant selectively reports positive information to the experimenter

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12
Q

Behavioural Bias

A

Expectency can cause participants to behave differently

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13
Q

4 types of participant

A

Good, Bad, Faithful, Apprehensive

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14
Q

Good Participant

A

Confirms experimenters hypothesis

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15
Q

Bad Participant

A

Disconfirms Ha

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16
Q

Faithful Participant

A

Follows instructions to the t

17
Q

Apprehensive Participant

A

Unusually concerned with experimenter’s evaluation of him/her

18
Q

Pygmalion Effect

A

Experimenter causes real changes in participants due to changes in his/her behavior during the experiment.

19
Q

Hawthorne Effect

A

Refers to any scenario in which participants alter their behavior because they’re being watched

20
Q

Halo Effect

A

Uncontrolled novelty of treatment (demonstrate sig improvements in the short term (8 months))

21
Q

Placebo effect

A

Spontaneous remission, non-specific effects & re-interpretation of ourcome measures

22
Q

Biosocial Experimenter cues

A

Age, sex & attractiveness –> for age & sex, can be determined by congruence, or it can be a constant effect

23
Q

Psychosocial Experimenter cues

A

Warmth, status, etc.

24
Q

Reducing Expectancy Effects

A

Standardize experimenter-participant interaction, use blinding techniques, use deception, convince participants that you can detect lying.

25
Q

Safeguards against misleading studies

A

Competition for funding, disseminated results in a peer reviewed journal & replication guards from T2 errors and invisible bias