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Midterm

# PSYC 102 Midterm: PSYCMIDTERM1REVIEW

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Department
Psychology
Course
PSYC 102
Professor
Darko Odic
Semester
Fall

Description
Problems with pure observation: - Bias - Problem of Complexity - Problem of Variability: We act differently from one another. Our action is not consistent. - Problem of Reactivity (Demand characteristics): We act differently when we think that we are being observed. To solve those problems, we use - Science Tools: measuring human thoughts and behaviours that give us precision and eliminate biases (operational definition, instruments, data, descriptive stat) - Science Rules: deciding what we observed that minimize the chance of biases and help us formulate theories (openness, falsifiable hypotheses, double-blind experiments, ethics) - Objective Measurements (Operational Definition): a description of a property in concrete, measurable ways (ex. Performance on midterm) through instruments. Good instrument - Validity: measuring what it purports to measure - Reliability: giving similar measurements each time - Power/Sensitivity: detecting even very small differences in the measure Descriptive Statistics: set of mathematical tools that help us describe data - Central tendency: statistic that describes the data’s typical value - Variability: how different the data points are from one another (range, standard deviation) Ethics: data is acquired in a manner that is fair to the participants (data is not faked, plagiarized or altered) Naturalistic observation: observing participants in the wild - No demand characteristics - High external validity (behaviours likely to be real, able to be generalized) - Cannot tell which aspects of the environment caused the behaviour - Have to tell people (usually) - High possibility of experimenter bias - Case Studies: observing and measuring the behaviour of an individual - Detect rare or unique cases - Useful for falsifying hypotheses - Low external validity: unable to be generalized Correlations: measuring relationship between two variables/ data sets - Directionality Problem: unable to tell which variable causes the other - Third Variable Problem Experiments: gathering data where one variable is manipulated in order to see its causual effect on another variable (independent or dependent / Random selection) Inferential statistics: Mathematical tools that help us make decisions with data - False Alarm: accepting an event that occurred by chance as meaningful - Miss: rejecting a real effect and attributing it to chance - Statistical significance: data values are unlikely due to chance - Large & Random samples Eugenics: social movement aimed at improving the human ‘genetic tool’ through selective breeding
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