Confidence intervals (CIs) and effect sizes are essential to encourage meta-analytic thinking and to accumulate research findings. CIs provide a range of plausible values for population parameters with a degree of confidence that the parameter is in that particular interval. CIs also give information about how precise the estimates are. Comparison of CIs across various samples cumulates evidence for more accurate estimates of population parameters than an estimate from a single sample. Effect sizes, on the other hand, provide information about the magnitude, or the substantive significance. That is, effect sizes provide information about sizes of differences between groups or strengths of relationships. The current paper's purpose is to inform researchers about the importance of reporting and interpreting CIs and effect sizes using examples from studies published in the Career and Technical Education Research journal. In addition, calculations and interpretations of CIs around means and effect sizes for some commonly used statistical analyses are explained. (Contains 3 tables and 3 figures.)