Properties of a Good Experiment PDF

Properties of a Good Experiment 1. Vary at least one independent variable: This allows researchers to observe the effects of different conditions on the dependent variable. 2. Randomly assign participants to conditions: Ensures that participants are initially equivalent across con...

Properties of a Good Experiment 1. Vary at least one independent variable: This allows researchers to observe the effects of different conditions on the dependent variable. 2. Randomly assign participants to conditions: Ensures that participants are initially equivalent across conditions, reducing bias. 3. Control extraneous variables: Helps eliminate confounding factors that could influence the results. Criteria for Causality To establish a causal relationship, the following criteria must be met: 1. Correlation: There must be a statistical association between the independent and dependent variables. 2. Temporal precedence: The cause must occur before the effect. 3. Non-spurious relationship: The observed relationship should not be due to a third variable (it should be genuine and not comparable). Probability Testing & Statistical Significance Probability testing: Used to determine the likelihood of a particular outcome, reflected by p-values. ○ Cutoffs: Typically, p < 0.05 or p < 0.01 are used. ○ A smaller p-value indicates greater statistical significance. Hypothesis Testing Definition: A systematic way to decide if the results of a study can be generalized to a larger population. Theory: Provides a conceptual framework for understanding how something works. Hypothesis: A specific, testable prediction derived from a theory. Characteristics of Good Hypotheses and Theories 1. Testable: They can be examined through research and experimentation. 2. Falsifiable: They can be proven wrong based on evidence.  Researchers consider the probability that the experimental procedure had no effect. If this probability is very small, they can infer that the results were due to the independent variable. Inferential Statistics Used when it is impractical or impossible to measure the entire population. Steps in Hypothesis Testing 1. Restate the research question: Formulate the research (alternative) hypothesis and the null hypothesis (H0: nothing happened). 2. Determine characteristics of the comparison distribution: Define the population distribution against which the sample will be compared. 3. Determine the cutoff sample score: Identify the score on the comparison distribution at which the null hypothesis should be rejected. 4. Determine your sample's score: Calculate the sample's score on the comparison distribution. 5. Decide whether to reject the null hypothesis: ○ Null hypothesis (H0): Assumes no effect or change (e.g., H0: μ1 = μ2). ○ Alternative hypothesis (H1): Indicates a significant effect or difference (e.g., H1: μ1 ≠ μ2). Nondirectional hypothesis: Indicates interest in any change, whether an increase or a decrease. Population vs. Sample Notation Use population notation rather than sample notation because the conclusions drawn are intended to generalize to the entire population, not just the sample used in the study. Properties of a Good Experiment: 1. Vary at least one independent variable: Manipulate a variable to observe its effect. 2. Randomly assign participants: Ensures groups are initially equivalent. 3. Control extraneous variables: Minimize outside factors that could influence the results. Criteria for Causality:  1. Correlation: A relationship must exist between the cause and effect. 2. Temporal Precedence: The cause must occur before the effect. 3. Non-Spurious Relationship: The relationship cannot be explained by a third factor. Probability Testing and Statistical Significance: p-values: Measure the likelihood that an observed effect occurred by chance. ○ Common cutoffs: p