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In the
statistical Statistics (from German: '' Statistik'', "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industr ...
theory of the
design of experiments The design of experiments (DOE, DOX, or experimental design) is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associ ...
, blocking is the arranging of experimental units in groups (blocks) that are similar to one another. Blocking can be used to tackle the problem of
pseudoreplication Pseudoreplication (sometimes unit of analysis error) has many definitions. Pseudoreplication was originally defined in 1984 by Stuart H. Hurlbert as the use of inferential statistics to test for treatment effects with data from experiments where ...
.


Use

Blocking reduces unexplained variability. Its principle lies in the fact that variability which cannot be overcome (e.g. needing two batches of raw material to produce 1 container of a chemical) is confounded or aliased with a(n) (higher/highest order) interaction to eliminate its influence on the end product. High order
interactions Interaction is action that occurs between two or more objects, with broad use in philosophy and the sciences. It may refer to: Science * Interaction hypothesis, a theory of second language acquisition * Interaction (statistics) * Interactions ...
are usually of the least importance (think of the fact that temperature of a reactor or the batch of raw materials is more important than the combination of the two - this is especially true when more (3, 4, ...) factors are present); thus it is preferable to confound this variability with the higher interaction.


Examples

*Male and Female: An experiment is designed to test a new drug on patients. There are two levels of the treatment, ''
drug A drug is any chemical substance that causes a change in an organism's physiology or psychology when consumed. Drugs are typically distinguished from food and substances that provide nutritional support. Consumption of drugs can be via inhala ...
'', and ''
placebo A placebo ( ) is a substance or treatment which is designed to have no therapeutic value. Common placebos include inert tablets (like sugar pills), inert injections (like Saline (medicine), saline), sham surgery, and other procedures. In general ...
'', administered to ''male'' and ''female'' patients in a double blind trial. The sex of the patient is a ''blocking'' factor accounting for treatment variability between ''males'' and ''females''. This reduces sources of variability and thus leads to greater precision. *Elevation: An experiment is designed to test the effects of a new pesticide on a specific patch of grass. The grass area contains a major elevation change and thus consists of two distinct regions - 'high elevation' and 'low elevation'. A treatment group (the new pesticide) and a placebo group are applied to both the high elevation and low elevation areas of grass. In this instance the researcher is blocking the elevation factor which may account for variability in the pesticide's application. *Intervention: Suppose a process is invented that intends to make the soles of shoes last longer, and a plan is formed to conduct a field trial. Given a group of ''n'' volunteers, one possible design would be to give ''n/2'' of them shoes with the new soles and ''n/2'' of them shoes with the ordinary soles, randomizing the assignment of the two kinds of soles. This type of experiment is a completely randomized design. Both groups are then asked to use their shoes for a period of time, and then measure the degree of wear of the soles. This is a workable experimental design, but purely from the point of view of statistical accuracy (ignoring any other factors), a better design would be to give each person one regular sole and one new sole, randomly assigning the two types to the left and right shoe of each volunteer. Such a design is called a "randomized complete
block design In combinatorial mathematics, a block design is an incidence structure consisting of a set together with a family of subsets known as ''blocks'', chosen such that frequency of the elements satisfies certain conditions making the collection of bl ...
." This design will be more sensitive than the first, because each person is acting as his/her own control and thus the
control group In the design of experiments, hypotheses are applied to experimental units in a treatment group. In comparative experiments, members of a control group receive a standard treatment, a placebo, or no treatment at all. There may be more than one t ...
is more closely matched to the treatment group block design In the
statistical Statistics (from German: '' Statistik'', "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industr ...
theory of the
design of experiments The design of experiments (DOE, DOX, or experimental design) is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associ ...
, blocking is the arranging of experimental units in groups (blocks) that are similar to one another. Typically, a blocking factor is a source of variability that is not of primary interest to the experimenter. An example of a blocking factor might be the sex of a patient; by blocking on sex, this source of variability is controlled for, thus leading to greater accuracy. When studying Probability Theory the blocks method consists of splitting a sample into blocks (groups) separated by smaller subblocks so that the blocks can be considered almost independent. The blocks method helps proving limit theorems in the case of dependent random variables. The blocks method was introduced by S. Bernstein: The method was successfully applied in the theory of sums of dependent random variables and in extreme value theory.Novak S.Y. (2011) Extreme Value Methods with Applications to Finance. Chapman & Hall/CRC Press, London.


Blocking used for nuisance factors that can be controlled

When we can control nuisance factors, an important technique known as blocking can be used to reduce or eliminate the contribution to experimental error contributed by nuisance factors. The basic concept is to create homogeneous blocks in which the nuisance factors are held constant and the factor of interest is allowed to vary. Within blocks, it is possible to assess the effect of different levels of the factor of interest without having to worry about variations due to changes of the block factors, which are accounted for in the analysis.


Definition of blocking factors

A nuisance factor is used as a blocking factor if every level of the primary factor occurs the same number of times with each level of the nuisance factor. The analysis of the experiment will focus on the effect of varying levels of the primary factor within each block of the experiment.


Block a few of the most important nuisance factors

The general rule is: :"Block what you can; randomize what you cannot." Blocking is used to remove the effects of a few of the most important nuisance variables. Randomization is then used to reduce the contaminating effects of the remaining nuisance variables. For important nuisance variables, blocking will yield higher significance in the variables of interest than randomizing.


Table

One useful way to look at a randomized block experiment is to consider it as a collection of completely randomized experiments, each run within one of the blocks of the total experiment. with :''L''1 = number of levels (settings) of factor 1 :''L''2 = number of levels (settings) of factor 2 :''L''3 = number of levels (settings) of factor 3 :''L''4 = number of levels (settings) of factor 4 ::\vdots :''Lk'' = number of levels (settings) of factor ''k''


Example

Suppose engineers at a semiconductor manufacturing facility want to test whether different wafer implant material dosages have a significant effect on resistivity measurements after a diffusion process taking place in a furnace. They have four different dosages they want to try and enough experimental wafers from the same lot to run three wafers at each of the dosages. The nuisance factor they are concerned with is "furnace run" since it is known that each furnace run differs from the last and impacts many process parameters. An ideal way to run this experiment would be to run all the 4x3=12 wafers in the same furnace run. That would eliminate the nuisance furnace factor completely. However, regular production wafers have furnace priority, and only a few experimental wafers are allowed into any furnace run at the same time. A non-blocked way to run this experiment would be to run each of the twelve experimental wafers, in random order, one per furnace run. That would increase the experimental error of each resistivity measurement by the run-to-run furnace variability and make it more difficult to study the effects of the different dosages. The blocked way to run this experiment, assuming you can convince manufacturing to let you put four experimental wafers in a furnace run, would be to put four wafers with different dosages in each of three furnace runs. The only randomization would be choosing which of the three wafers with dosage 1 would go into furnace run 1, and similarly for the wafers with dosages 2, 3 and 4.


Description of the experiment

Let ''X''1 be dosage "level" and ''X''2 be the blocking factor furnace run. Then the experiment can be described as follows: :''k'' = 2 factors (1 primary factor ''X''1 and 1 blocking factor ''X''2) :''L''1 = 4 levels of factor ''X''1 :''L''2 = 3 levels of factor ''X''2 :''n'' = 1 replication per cell :''N'' = ''L''1 * ''L''2 = 4 * 3 = 12 runs Before randomization, the design trials look like:


Matrix representation

An alternate way of summarizing the design trials would be to use a 4x3 matrix whose 4 rows are the levels of the treatment ''X''1 and whose columns are the 3 levels of the blocking variable ''X''2. The cells in the matrix have indices that match the ''X''1, ''X''2 combinations above. By extension, note that the trials for any K-factor randomized block design are simply the cell indices of a ''k'' dimensional matrix.


Model

The model for a randomized block design with one nuisance variable is : Y_ = \mu + T_i + B_j + \mathrm where :''Y''ij is any observation for which ''X''1 = ''i'' and ''X''2 = ''j'' :''X''1 is the primary factor :''X''2 is the blocking factor :μ is the general location parameter (i.e., the mean) :''T''i is the effect for being in treatment ''i'' (of factor ''X''1) :''B''j is the effect for being in block ''j'' (of factor ''X''2)


Estimates

:Estimate for μ : \overline = the average of all the data :Estimate for ''T''i : \overline_ - \overline with \overline_ = average of all ''Y'' for which ''X''1 = ''i''. :Estimate for ''B''j : \overline_ - \overline with \overline_ = average of all ''Y'' for which ''X''2 = ''j''.


Generalizations

* Generalized randomized block designs (GRBD) allow tests of block-treatment interaction, and has exactly one blocking factor like the RCBD. * Latin squares (and other row-column designs) have two blocking factors that are believed to have no interaction. *
Latin hypercube sampling Latin hypercube sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. The sampling method is often used to construct computer experiments or for Monte Carlo integratio ...
* Graeco-Latin squares * Hyper-Graeco-Latin square designs


Theoretical basis

The theoretical basis of blocking is the following mathematical result. Given random variables, ''X'' and ''Y'' : \operatorname(X-Y)= \operatorname(X) + \operatorname(Y) - 2\operatorname(X,Y). The difference between the treatment and the control can thus be given minimum variance (i.e. maximum precision) by maximising the covariance (or the correlation) between ''X'' and ''Y''.


See also

*
Algebraic statistics Algebraic statistics is the use of algebra to advance statistics. Algebra has been useful for experimental design, parameter estimation, and hypothesis testing. Traditionally, algebraic statistics has been associated with the design of experiments ...
*
Block design In combinatorial mathematics, a block design is an incidence structure consisting of a set together with a family of subsets known as ''blocks'', chosen such that frequency of the elements satisfies certain conditions making the collection of bl ...
* Combinatorial design * Generalized randomized block design *
Glossary of experimental design A glossary of terms used in experimental research. Concerned fields * Statistics *Experimental design *Estimation theory Glossary * Alias: When the estimate of an effect also includes the influence of one or more other effects (usually high ...
*
Optimal design In the design of experiments, optimal designs (or optimum designs) are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credited to Danish statistic ...
*
Paired difference test In statistics, a paired difference test is a type of location test that is used when comparing two sets of measurements to assess whether their population means differ. A paired difference test uses additional information about the sample that i ...
*
Dependent and independent variables Dependent and independent variables are variables in mathematical modeling, statistical modeling and experimental sciences. Dependent variables receive this name because, in an experiment, their values are studied under the supposition or dema ...
*
Blockmodeling Blockmodeling is a set or a coherent framework, that is used for analyzing social structure and also for setting procedure(s) for partitioning (clustering) social network's units ( nodes, vertices, actors), based on specific patterns, which form ...


References

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Bibliography

* * * * Pre-publication chapters are available on-line. * * * * * * ** ** * * * * * * * {{DEFAULTSORT:Blocking (Statistics) Design of experiments