Supernatural Explanations: Attributes behavior to non-physical forces like spirits and deities.
Animism: Belief that natural phenomena are alive and influence behavior.
Example: Possessing an eagle’s feather grants certain properties to the owner.
Mythology and Religion: Non-physical forces affect human behavior, differing from scientific assumptions.
Astrology: Suggests that celestial bodies influence human behavior and predict actions.
Shift towards logic and empirical observation.
Empiricism (David Hume): Knowledge should be based on observations.
Positivism (Auguste Comte): Focus on knowledge derived solely from sense perceptions.
Intuition: Knowledge based on instinctive feeling rather than conscious reasoning.
Problem: Conclusions often drawn without sufficient evidence.
Authority: Trusting figures of authority to provide information.
Problem: Questioning the authenticity of the authority.
Scientific Skepticism: Withholding judgment and systematically evaluating claims helps consider all possibilities.
Science: Requires adapting views based on new evidence.
Determinism: The universe operates in a systematic, orderly manner; events have meaningful causes.
Causation:
Covariation of Cause and Effect: Presence of cause corresponds with presence of effect.
Temporal Precedence: Cause must occur before the effect.
Elimination of Alternative Explanations: Ensuring no other variable influences the effect.
Objectives:
Describe behavior.
Predict behavior.
Determine causes of behavior.
Explain behavior.
Basic Research: Addresses fundamental questions.
Applied Research: Focuses on practical problems.
Replication: Detailed description for duplicating studies and results.
Testability/Falsifiability: Rejecting untestable ideas.
Peer Review: Validating research to ensure study quality.
Adversarial Process: Evaluating opposing theories for experimental comparison.
Unfalsifiable hypotheses.
Non-scientific methodology.
Anecdotal evidence or reliance on authority.
Lack of peer-reviewed citations and revisions based on new data.
Ignoring conflicting evidence.
Biorhythms: Claiming human behavior follows physical, emotional, and intellectual cycles of specific durations.
Homeopathy: Substance that induces symptoms in healthy individuals cures similar symptoms in patients.
Phrenology: Inferring personality traits based on skull bumps.
Assess data source for credibility.
Evaluate methods of study conduct.
Analyze statistical methods used.
Review conclusions drawn from analysis.
Why Statistics?: Trust issues due to biases; control of extraneous variables; direct study limitations necessitating statistical methods.
Statistics Defined: Method of understanding data, aiding in decision-making.
Descriptive Statistics: Numbers summarizing data (mean, median, standard deviation).
Inferential Statistics: Making predictions about a population from sample data.
Theory: General statements on relationships among variables, providing organizational frameworks and generating new knowledge.
Sample characteristics, reasons for participation, controls, sample size, wording, causation, funding sources, and peer-reviewed publication.
Population: Entire group of interest.
Sample: Subset of the population tested.
Independent Variable (IV): Manipulated variable.
Dependent Variable (DV): Measured outcome.
Construct: Internal attribute not directly observable.
Operational Definition: Specifies procedures to represent a construct, e.g., measuring hunger.
Variable Types:
Discrete Variable: Indivisible categories (e.g., number of children).
Continuous Variable: Measurable characteristics (e.g., height, weight).
Dichotomous Variable: Only two possible outcomes.
Nominal Scale: Categorizes data without implying order. Frequency calculations only; no intermediate values.
Ordinal Scale: Includes order but does not quantify the gap between points.
Interval Scale: Provides numeric intervals but lacks a true zero point; zero is arbitrary (e.g., temperature).
Ratio Scale: Has all properties of interval scales but includes an absolute zero.
Mean: Average score; influenced by extremes but computationally simple.
Mode: Most common score in a dataset; relevant for nominal scales.
Median: Middle value dividing data into halves; especially useful in skewed distributions.
Variability Defined: Quantifies how spread out the scores are within a distribution.
Range: Difference between highest and lowest scores; influenced by extremes.
Interquartile Range: Range of middle 50% of data, calculated as Q3-Q1.
Standard Deviation: Average distance from the mean, widely used measure of variability.
Key Ethical Organizations: CIHR, NSERC, SSHRC.
Concerns: Protecting subjects from risks, informed consent, and ethical conduct in experiments.
Experiments During WWII: Resulted in establishing ethical guidelines (Nuremberg Code, Helsinki Declaration, Belmont Report).
Milgram Study Insights: Experiment demonstrating obedience—65% of subjects administered maximum shocks when instructed.
Respect for Persons: Ensuring voluntary participation.
Concern for Welfare: Balancing risks against potential benefits.
Justice: Fair treatment of participants.
Consider psychological, physical, and privacy-related risks while weighing the benefits to society.
Required prior to participation; ensuring participants are fully briefed.
Definition: Percentiles indicate the position of a score within a distribution.
Purpose of z-scores: Standardize scores by indicating how many standard deviations they are from the mean.
Good Sample Characteristics: Representative and sufficiently large to minimize errors.
Central Limit Theorem (CLT): As sample size increases, the distribution of sample means approaches normality.
Distinction between descriptive (summarizes data) and inferential statistics (draws conclusions about populations).
Differences between z-tests and t-tests based on population standard deviation availability.
Clarification of designs: Independent samples versus dependent samples in testing means.
Importance of understanding correlation and causation; types of correlations analyzed.
Primary concepts include analytical probability, mutual exclusivity, and addition/multiplication rules.
Types of Studies: Differentiating experimental from non-experimental designs.
Discussing reliability, validity, and various types of reliability and validity tests.
Overview of observational methods including naturalistic and systematic observation, as well as concerns inherent in qualitative research.