ABSTRACT
Much everyday thinking is like the scientific method of hypothesis testing: formulated belief and supporting observations determine if expectations are correct. Using induction, we derive hypotheses from observations. Deduction entails collecting observations that confirm or disconfirm our hypotheses. Most thought involves interplay between these processes. Precise definition and measurement are hallmarks of operational definitions. Independent variables predict or explain dependent variables. Hypotheses explain the effect of independent variable(s) on dependent variable(s). When drawing conclusions, using adequately large sample sizes are important because people vary in their responses. Most people willingly generalize from results obtained from small samples. To appropriately generalize, a sample must be representative of its population, which means recognizing sample bias(es) before generalizing. To determine if one variable (e.g., smoking) causes another (e.g., lung cancer), the causal variables must be isolated and controlled. Related variables—where changes in one are associated with changes in another—are correlated variables. Correlations are positive, negative, or zero in valence, and correlation does not imply causation. Inadvertently, we may display self-fulfilling prophecies by acting in ways that confirm or disconfirm hypotheses according to our expectations.
