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18 Cards in this Set

  • Front
  • Back

confounding of variables

2 variables are intertwined so we can't determine which one influenced the DV

placebo effects

people receiving a treatment show a change in behavior because of their expectations, not because the treatment was working

experimenter expectancy effects

subtle unintentional ways a researcher influences participants to respond consistent with the researcher's hypothesis

Replicating and generalizing the findings

the study should be able to be replicated with some results to generalize the findings with respect to a total population

social desirability bias

when participants in a survey answer with the socially/culturally acceptable answer instead of the honest one

sampling bias

sample population may not be randomly spread and may contain more of one group than others on accident

external validity

the degree to which the results can be generalized to other populations; results must indicate the same issue

Mode

most frequent data point

median

middle data point

mean

average value of data points

variability

the degree of variation or spread in scores (range= highest - lowest, standard deviation= how much each score differs from the mean)

correlation coefficient

(-1.0)-(1.0) representing the strength and type of relation between 2 variables

positive correlation

higher scores on X is found with higher Y scores

negative correlation

Higher scores on X is found with lower Y scores

strong vs weak correlation

closer to -1 or 1 is stronger correlation (shown on scatter plot)

correlation and causation

correlation can predict behaviors from a lab to the real world but cannot imply causation because we don't know which variable x or y is doing the influencing

descriptive statistics

allows the summary and description of the characteristics of a set (correlation coefficient)

inferential statistics

tells us how confident we are in making inferences about a population based on findings obtained from a sample