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@@ -664,7 +664,7 @@ <h3 class="anchored" data-anchor-id="random-data">Random Data</h3>
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 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">sample</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">4</span>,<span class="dv">5</span>,<span class="dv">6</span>), <span class="dv">25</span>, <span class="at">replace=</span><span class="cn">TRUE</span>)</span></code></pre></div>
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-<pre><code> [1] 1 2 6 2 5 6 3 4 5 1 3 2 1 4 2 1 6 6 6 3 4 3 4 3 2</code></pre>
+<pre><code> [1] 4 1 4 2 3 1 1 6 2 6 6 2 1 4 2 6 3 2 6 2 3 5 3 3 6</code></pre>
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@@ -683,8 +683,8 @@ <h3 class="anchored" data-anchor-id="random-data">Random Data</h3>
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 <span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>assignment</span></code></pre></div>
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-<pre><code> [1] treatment placebo   treatment placebo   treatment placebo   placebo  
- [8] treatment treatment placebo  
+<pre><code> [1] treatment placebo   placebo   placebo   treatment placebo   treatment
+ [8] treatment placebo   treatment
 Levels: placebo treatment</code></pre>
 </div>
 </div>
@@ -743,11 +743,11 @@ <h2 class="anchored" data-anchor-id="oodles-of-observations">Oodles of Observati
 <span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(demographics)</span></code></pre></div>
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 <pre><code>  age biosex
-1  43 female
-2  23   male
-3  68   male
-4  56 female
-5  57   male
+1  18 female
+2  49 female
+3  36 female
+4  25 female
+5  30 female
 6  77 female</code></pre>
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Labs/Lab02.html

@@ -8,7 +8,7 @@
 
 <meta name="author" content="Michael Pascale">
 
-<title>Visualizing Z, t, p – Introduction to Experimental Design</title>
+<title>Making Decisions via the Sampling Distribution – Introduction to Experimental Design</title>
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@@ -509,7 +509,7 @@
     <h2 id="toc-title">On this page</h2>
 
   <ul>
-  <li><a href="#reviewing-p-values" id="toc-reviewing-p-values" class="nav-link active" data-scroll-target="#reviewing-p-values">Reviewing P-Values</a></li>
+  <li><a href="#visualizing-p-values" id="toc-visualizing-p-values" class="nav-link active" data-scroll-target="#visualizing-p-values">Visualizing P-Values</a></li>
   <li><a href="#visualizing-the-confidence-interval" id="toc-visualizing-the-confidence-interval" class="nav-link" data-scroll-target="#visualizing-the-confidence-interval">Visualizing the Confidence Interval</a></li>
   <li><a href="#visualizing-the-t-distribution" id="toc-visualizing-the-t-distribution" class="nav-link" data-scroll-target="#visualizing-the-t-distribution">Visualizing the <span class="math inline">\(t\)</span>-Distribution</a></li>
   </ul>
@@ -521,7 +521,7 @@ <h2 id="toc-title">On this page</h2>
 
 <header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../Labs/Lab01.html">Lab Components</a></li><li class="breadcrumb-item"><a href="../Labs/Lab08.html">Visualizing Z, t, p</a></li></ol></nav>
 <div class="quarto-title">
-<h1 class="title">Visualizing Z, t, p</h1>
+<h1 class="title">Making Decisions via the Sampling Distribution</h1>
 <p class="subtitle lead">Lab 8</p>
 </div>
 
@@ -551,8 +551,8 @@ <h1 class="title">Visualizing Z, t, p</h1>
 </header>
 
 
-<section id="reviewing-p-values" class="level2 page-columns page-full">
-<h2 class="anchored" data-anchor-id="reviewing-p-values">Reviewing P-Values</h2>
+<section id="visualizing-p-values" class="level2 page-columns page-full">
+<h2 class="anchored" data-anchor-id="visualizing-p-values">Visualizing P-Values</h2>
 <p>Recall that with the <strong><span class="math inline">\(Z\)</span>-statistic</strong> we compare a sample to <strong>normally distributed samples</strong> from the same population. Let’s try to understand this visually.</p>
 <p>Say we obtain a sample mean <span class="math inline">\(\bar x\)</span> with a <span class="math inline">\(Z\)</span>-statistic of <span class="math inline">\(1.3\)</span>. This tells us that the sample lies <span class="math inline">\(1.3\)</span> times the S.E.M. above <span class="math inline">\(\mu\)</span> on the sampling distribution.</p>
 <p>To calculate the <strong>p-value</strong>, we need to consider a particular <strong>hypothesis</strong>. In the case that <span class="math inline">\(H_0: \mu = \mu_0\)</span> and <span class="math inline">\(H_A: \mu \neq \mu_0\)</span>, then the p-value represents the probability under the <em>null hypothesis</em> of a value at least as extreme, in either the positive or negative direction. In other words, the <strong>two-tailed</strong> area under the curve.</p>
@@ -635,6 +635,7 @@ <h2 class="anchored" data-anchor-id="visualizing-the-t-distribution">Visualizing
 </div>
 <p>Consider the one-tailed (left) p-value for <span class="math inline">\(t=-1.21\)</span> will be <span class="math inline">\(0.1225\)</span>.</p>
 <p><strong>QUESTION</strong>: <em>What is the <strong>two-tailed</strong> p-value for <span class="math inline">\(t=-1.6\)</span>?</em></p>
+<p><strong>QUESTION</strong>: <em>Would you have concluded something different if you had set out to do a left-sided test? A right-sided test?</em></p>
 
 
 </section>