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Understanding T-Tests: Evaluating Differences Between Mean Values in Experimental Research

This article explores the concept of T-Tests, focusing on null hypotheses, which state any differences in two mean values may be due to chance. We discuss the Independent T-Test's role in assessing whether the independent variable (IV) affects the dependent variable (DV) by comparing two groups: an experimental group that receives the IV and a control group that does not. Additionally, we examine the Dependent T-Test for pre-and post-measure significance within a single group undergoing two treatments, highlighting the importance of these statistical tests in research methodologies.

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Understanding T-Tests: Evaluating Differences Between Mean Values in Experimental Research

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  1. T-Test Summary

  2. Null Hypothesis • Any observable difference between two mean values is due to chance • Determine whether there is a significant difference between IV and DV • IV: alleged cause • V: outcome

  3. Independent T-test • Effect of IV on DV • Two independent groups: experimental and control • Experimental group receives IV • Control group does not • Determine whether the means were signficant

  4. Dependent T-test • Determine significance from pre to post measures • One group two treatments

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