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Statistics Dictionary Absolute Value Accuracy Addition Rule Alpha Alternative Hypothesis ANOVA Back-to-Back Stemplots Balanced Design Bar Chart Bartlett's Test Bayes Rule Bayes Theorem Bias Biased Estimate Bimodal Distribution Binomial Distribution Binomial Experiment Binomial Probability Binomial Random Variable Bivariate Data Blinding Blocking Blocking Variable Bonferroni Correction Boxplot Cartesian Plane Categorical Variable Census Central Limit Theorem Chi-Square Distribution Chi-Square Goodness of Fit Test Chi-Square Statistic Chi-Square Test for Homogeneity Chi-Square Test for Independence Cluster Cluster Sampling Coefficient of Determination Coefficient of Multiple Determination Column Vector Combination Comparisons Complement Completely Randomized Design Conditional Distribution Conditional Frequency Conditional Probability Confidence Interval Confidence Level Confounding Contingency Table Continuous Probability Distribution Continuous Variable Control Group Convenience Sample 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Binomial Probability Negative Binomial Random Variable Neyman Allocation Nominal Scale Nonlinear Transformation Non-Probability Sampling Nonresponse Bias Normal Distribution Normal Random Variable Null Hypothesis Null Set Observational Study One-Sample t-Test One-Sample z-Test One-stage Sampling One-Tailed Test One-Way ANOVA One-Way Table Optimum Allocation Ordinal Scale Orthogonal Comparisons Outer Product Outlier Paired Data Parallel Boxplots Parameter Pearson Product-Moment Correlation Percentage Percentile Permutation Placebo Planned Comparisons Point Estimate Poisson Distribution Poisson Experiment Poisson Probability Poisson Random Variable Population Post Hoc Comparisons Power Precision Probability Probability Density Function Probability Distribution Probability Sampling Proportion Proportionate Stratification P-Value Qualitative Variable Quantitative Variable Quartile Random Effects Model Random Factor Random Number Table Random Numbers Random Sampling Random Variable Randomization Randomized Block Design Randomized Block Experiment Range Ratio Scale Reduced Row Echelon Form Region of Acceptance Region of Rejection Regression Relative Frequency Relative Frequency Table Repeated Measures Design Replication Representative Residual Residual Plot Response Bias Row Echelon Form Row Vector Sample Sample Design Sample Point Sample Space Sample Survey Sampling Sampling Distribution Sampling Error Sampling Fraction Sampling Method Sampling With Replacement Sampling Without Replacement Scalar Matrix Scalar Multiple Scatterplot Scheffe Test Selection Bias Set Significance Level Simple Random Sampling Simple Regression Singular Matrix Skewness Slope Sphericity Standard Deviation Standard Error Standard Normal Distribution Standard Score Statistic Statistical Experiment Statistical Hypothesis Statistics Stemplot Strata Stratified Sampling Subset Subtraction Rule Sum Vector Sums of Squares Symmetric Matrix Symmetry Systematic Sampling T Distribution T Score T Statistic Test Statistic Transpose Treatment t-Test Two-Sample t-Test Two-stage Sampling Two-Tailed Test Two-Way Table Type I Error Type II Error Unbiased Estimate Undercoverage Uniform Distribution Unimodal Distribution Union Univariate Data Variable Variance Variance Inflation Factor Vector Inner Product Vector Outer Product Vectors Venn Diagram Voluntary Response Bias Voluntary Sample Y Intercept z-score

Randomized Block Design

With a randomized block design, the experimenter divides subjects into subgroups called blocks, such that the variability within blocks is less than the variability between blocks. Then, subjects within each block are randomly assigned to treatment conditions. Compared to a completely randomized design, this design reduces variability within treatment conditions and potential confounding, producing a better estimate of treatment effects.

The table below shows a randomized block design for a hypothetical medical experiment.

Subjects are assigned to blocks, based on gender. Then, within each block, subjects are randomly assigned to treatments (either a placebo or a cold vaccine). For this design, 250 men get the placebo, 250 men get the vaccine, 250 women get the placebo, and 250 women get the vaccine.

It is known that men and women are physiologically different and react differently to medication. This design ensures that each treatment condition has an equal proportion of men and women. As a result, differences between treatment conditions cannot be attributed to gender. This randomized block design removes gender as a potential source of variability and as a potential confounding variable.

See also:

Experimental Design