Independence is critical for a meaningful statistical analysis. For example, what one study participant does should not influence what another participant does.
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Independence or Independently: When one factor does not exert influence on another.Hypothesis: A prediction of whether the independent variable will have an effect on the dependent variable or a prediction of the nature of the effect.Examples might include factors you consider unimportant at the time of an experiment, such as the manufacturer of the glassware in a reaction or the color of paper used to make a paper airplane. Extraneous Variable: Extra variables (not independent, dependent, or control variables) that might influence an experiment but are not accounted for or measured or are beyond control.Experimental Group: Test subjects randomly assigned to receive the experimental treatment.Empty Control Group: A type of control group that does not receive any treatment, including a placebo.Double-Blind: When neither the researcher nor the subject knows whether the subject is receiving the treatment or a placebo.Also known as the dependent measure or responding variable. The dependent variable is the one being measured in the experiment. Dependent Variable: The variable that responds to the independent variable.Data (singular: datum): Facts, numbers, or values obtained in an experiment.Control Variable: Any variable that does not change during an experiment.Control Group: Test subjects randomly assigned to not receive the experimental treatment.Conclusion: Determination of whether the hypothesis should be accepted or rejected.A normally distributed sample mean is necessary to apply the t-test, so if you are planning to perform a statistical analysis of experimental data, it's important to have a sufficiently large sample.
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Central Limit Theorem: States that with a large enough sample, the sample mean will be normally distributed.