anchor.diff:                              If PhenStat assumptions about the input data are not met, a second attempt at analyzing the data
                               will be attempted &mdash; a Mann-Whitney U Rank Sum test.
                               </p>
  -                            
  +
                               <h4>Control selection strategy</h4>
  -                            
  +
                               <p>One side effect of producing data in a high throughput pipeline is that the input data for a statistical calculation might be produced over multiple days.
  -                            Environmental fluctuations have been identified as a confounding factor when comparing 
  +                            Environmental fluctuations have been identified as a confounding factor when comparing
                               data gathered on different days.  The IMPC describes this as a "batch effect" and it is treated as a random effect in the Mixed model framework.
                               </p>
  -                            
  +
                               <p>The data sets to be analysed are identified using unique combinations of these fields:
  -                            
  +
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                              <h3>More information about the way IMPC uses statistics.</h3>
  
                             <p>
                              High-throughput phenotyping generates large volumes of varied data including
                              both categorical and continuous data. Operational and cost constraints can
                              lead to a work-flow that precludes traditional analysis methods. Furthermore,
                              for a high throughput environment, a robust automated statistical pipeline
                              that alleviates manual intervention is required.</p>
  
                              <p>The IMPC uses a variety of statistical methods for making phenotype calls, including:</p>
  
                              <ul>
                                  <li><a href="https://en.wikipedia.org/wiki/Fisher%27s_exact_test"><strong>Fisher's Exact test</strong></a> - used for categorical data parameters</li>
                                  <li><a href="https://en.wikipedia.org/wiki/Mixed_model"><strong>Mixed model</strong></a> - used for continuous data parameters which include random effects </li>
                                  <li><a href="https://en.wikipedia.org/wiki/Linear_model"><strong>Linear model</strong></a> - used for continuous data parameters when random effects are not significant</li>
                                  <li><a href="https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U"><strong>Mann-Whitney U Rank sum test</strong></a> - used for continuous data parameters when conditions for Mixed model are not appropriate</li>
                                  <li><a href="https://bioconductor.org/packages/release/bioc/vignettes/PhenStat/inst/doc/PhenStatUsersGuide.pdf"><strong>Reference Range Plus</strong></a> - used for some unidimensional data parameters</li>
                              </ul>
  
                              <p>
                              The Mixed model (MM), Fisher's Exact (FE), and Reference Range Plue (RR+) methods used have been formalized into an R package called
                              <a href="http://bioconductor.org/packages/release/bioc/html/PhenStat.html">PhenStat</a>.
                              See the <a href="http://bioconductor.org/packages/release/bioc/vignettes/PhenStat/inst/doc/PhenStatUsersGuide.pdf">complete PhenStat user's guide</a>.
                              </p>
  
                              <p>
                              All analysis frameworks output a statistical significance measure,
                              an effect size measure, model diagnostics (when appropriate),
                              and <a href="${baseUrl}/documentation/graph-help">graphical visualisation</a>.
                              </p>
  
                              <p>
                              The PhenStat package provides statistical methods for
                              the identification of abnormal phenotypes with an emphasis on high-throughput dataflows.
                              The package contains:</p>
                              <ul>
  	                            <li>dataset checks and cleaning in preparation for the analysis</li>
  	                            <li>2 statistical frameworks for genotype to phenotype identification
  		                            <ul>
  			                            <li>Fisher's Exact test for Categorical data</li>
  			                            <li>Linear Mixed model for continuous data</li>
                                          <li>Reference range plus model for low N continuous data</li>
  		                            </ul>
  	                            </li>
  	                            <li> and additional functions that help to decide the correct method for analysis.</li>
                              </ul>
  
                              <p>
                              The Mixed model framework assumes that base line values of the
                              dependent variable are normally distributed but batch (assay date)
                              adds noise and models variables accordingly in
                              order to separate the batch and the genotype. Model
                              optimisation starting with:</p>
  
                              <blockquote>
  