Factors Influencing Experimental Outcomes and Null Effects Quiz

Weather conditions are external factors not typically controlled in experiments.

Null effect indicates the absence of a significant change due to the treatment.

P-value measures the probability of obtaining results at least as extreme as the observed results.

Confirmation bias leads researchers to interpret information in a way that confirms their preconceptions.

The Hawthorne effect refers to changes in behavior due to awareness of being observed.

Placebo condition involves giving participants a fake treatment to control for psychological effects.

Confounding variables obscure the relationship between the independent and dependent variables.

ANOVA (Analysis of Variance) determines whether there are statistically significant differences between group means.

Standard deviation is a measure of data dispersion, not a source of bias in itself.

Random assignment ensures that participants are equally distributed across conditions, reducing the impact of confounding variables.

Demand characteristics are cues that lead participants to guess the researcher's hypothesis, potentially biasing their responses.

Experimenter expectancy occurs when researchers' beliefs or expectations about the outcome affect participants' behavior or results.

Type I error involves concluding that there is an effect when there isn't, leading to false positives.