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ttest.html
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<!DOCTYPE html>
<html>
<head>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-131282143-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-131282143-1');
</script>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<title>EZ Statistics: T-test</title>
<meta name="description" content="EZ Statistics t-test">
<link rel="stylesheet" href="style/stats.css">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.12.4/jquery.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery-csv/0.71/jquery.csv-0.71.min.js"></script>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
<script src="jstat.js"></script>
<script src="ezstatistics-0.30.js"></script>
</head>
<body onload="javascript:set_test_type()">
<center><img class="round" src="style/logo.png" height="105"/></center>
<div style="text-align: right"><a href="index.html">Back to main page</a></div>
<h3 class="f18b">T-test</h3>
Requires that the samples are normally distributed. Select the type of test depending on if the samples are independent or paired, and if sample variances are equal or not.
<br> <br>
Tests the hypotheses:
<table>
<tr>
<th class="dark" width="110">H<sub>0</sub></th>
<td class="border">There is no difference between the means of the samples </td>
</tr>
<tr>
<th class="dark">H<sub>1</sub> (two-tailed)</th>
<td class="border">The mean of sample A is different than the mean of sample B </td>
</tr>
<tr>
<th class="dark">H<sub>1</sub> (one-tailed)</th>
<td class="border">The mean of sample A is either less than or greater than the mean of sample B </td>
</tr>
</table>
<div class="smalltext">
<div class="label16">
<h3 class="f16"> Test Type</h3>
</div>
<br/>
<form action="" id="testtype">
<input class="type" type="radio" name="type" id="1" value="1" checked> Independent, equal variances<br>
<input class="type" type="radio" name="type" id="2" value="2"> Independent, unequal variances<br>
<input class="type" type="radio" name="type" id="3" value="3"> Paired
</form>
<div class="label16">
<h3 class="f16"> Test Hypothesis</h3>
</div>
<br/>
<form action="" id="testtype">
<input class="type" type="radio" name="hyp" id="1" value="1" checked> <b>≠</b> The mean of sample A is different than the mean of sample B (two-tailed)<br>
<input class="type" type="radio" name="hyp" id="2" value="2"> <b><</b> The mean of sample A is less than the mean of sample B (one-tailed)<br>
<input class="type" type="radio" name="hyp" id="3" value="3"> <b>></b> The mean of sample A is greater than the mean of sample B (one-tailed)
</form>
<div class="label16">
<h3 class="f16"> Data Entry</h3>
</div>
<br/>
<table>
<tr>
<td>Sample A:</td>
<td><input class="sample" name="sampA" id="samp1" value="13, 17, 19, 11, 20, 15, 18, 9, 12, 16"></td>
<tr/>
<tr>
<td>Sample B:</td>
<td><input class="sample" name="sampB" id="samp2" value="12, 8, 6, 16, 12, 14, 10, 18, 4, 11"></td>
<tr/>
<tr>
<td>Significance level α: </td>
<td><input class="value" name="alpha" id="alpha" value="0.05"></td>
</tr>
<tr>
<td>Upload CSV file:</td>
<td>
<input type="file" name="File Upload" id="txtFileUpload" accept=".csv" />
</td>
</tr>
</table>
<br>
<button class="test" onclick="javascript:run_ttest()">Run Test</button>
<button class="clear" onclick="javascript:clear_fields(2)">Clear</button>
<div id="error">
</div>
<div id="test_results" style="display: none;">
<div class="label16">
<h3 class="f16"> Test Result</h3>
</div>
<br/>
<table class="border">
<thead>
<tr>
<th colspan=4 class="dark">Data Summary</th>
</tr>
<tr>
<th class="dark" width="70">Sample</th>
<th class="dark" width="40">N</th>
<th class="dark" width="100">Mean</th>
<th class="dark" width="100">Stdev</th>
</tr>
</thead>
<tbody>
<tr>
<th class="dark">A</th>
<td class="border" id="n1"> </td>
<td class="border" id="mean1"> </td>
<td class="border" id="stdev1"> </td>
</tr>
<tr>
<th class="dark">B</th>
<td class="border" id="n2"> </td>
<td class="border" id="mean2"> </td>
<td class="border" id="stdev2"> </td>
</tr>
</tbody>
</table>
<br/>
<table class="border">
<thead>
<tr>
<th class="dark" width="550" colspan="2">Result</th>
</tr>
</thead>
<tbody>
<tr>
<td class="dark" width="130"><b>Mean<sub>A</sub> - Mean<sub>B</sub>:</b></td>
<td class="border" width="420" id="mean"> </td>
</tr>
<tr>
<td class="dark"><b>Significance level α:</b></td>
<td class="border" id="sign_level"> </td>
</tr>
<tr>
<td class="dark"><b>P-value:</b></td>
<td class="border" id="p"> </td>
</tr>
<tr>
<td class="dark"><b>T-score:</b></td>
<td class="border" id="t"> </td>
</tr>
<tr>
<td class="dark"><b>Result:</b></td>
<td class="border" id="res"> </td>
</tr>
</tbody>
</table>
<div id="power">
<div class="label16">
<h3 class="f16"> Power Analysis <button class="help" onclick="javascript:toggle('pahelp')";>?</button></h3>
</div>
<div id="pahelp" style="display: none;">
<br>
The first table shows the minimum required sample sizes for low, medium and high statistical power respectively.
The second table shows the current statistical power for the samples and test type.
</div>
<br>
<table class="border">
<thead>
<tr>
<th colspan=3 class="dark">Required sample sizes</th>
</tr>
<tr>
<th class="dark" width="120">Power</th>
<th class="dark" width="150">Min sample size</th>
</tr>
</thead>
<tbody>
<tr>
<td class="border">Low (20%)</td>
<td class="border" id="n_low"> </td>
</tr>
<tr>
<td class="border">Medium (50%)</td>
<td class="border" id="n_medium"> </td>
</tr>
<tr>
<td class="border">High (80%)</td>
<td class="border" id="n_high"> </td>
</tr>
</tbody>
</table>
<br>
<table class="border">
<thead>
<tr>
<th class="dark" width="150">Current power</th>
</tr>
</thead>
<tbody>
<tr>
<td class="border" id="pwr"> </td>
</tr>
</tbody>
</table>
</div>
<div id="assumptions">
<div class="label16">
<h3 class="f16"> Check test assumptions <button class="help" onclick="javascript:toggle('ashelp')";>?</button></h3>
</div>
<div id="ashelp" style="display: none;">
<br>
The samples should be normally distributed. If not, consider using <a href="wilcoxon.html">Wilcoxon tests</a> instead.
Note that the normality test is not entirely accurate for sample sizes under 20.
<p class="tab">Select correct test type depnding on if the variances are equal or not.</p>
</div>
<br>
<div id="vartest">
<table class="border">
<thead>
<tr>
<th class="dark" width="550" colspan="2">F-test for equal variances</th>
</tr>
</thead>
<tbody>
<tr>
<td class="dark" width="100"><b>Result:</b></td>
<td class="border" id="f_res"> </td>
</tr>
<tr>
<td class="dark"><b>P-value:</b></td>
<td class="border" id="f_p"> </td>
</tr>
</tbody>
</table>
<br>
</div>
<div id="normtest">
<table class="border">
<thead>
<tr>
<th class="dark" width="550" colspan="2">Shapiro-Wilk test for normally distributed samples</th>
</tr>
</thead>
<tbody>
<tr>
<td class="dark" colspan="2"><center><b>Sample A</b></center></td>
</tr>
<tr>
<td class="dark" width="100"><b>Result:</b></td>
<td class="border" id="sw_res_1"> </td>
</tr>
<tr>
<td class="dark"><b>P-value:</b></td>
<td class="border" id="sw_p_1"> </td>
</tr>
<tr>
<td class="dark" colspan="2"><center><b>Sample B</b></center></td>
</tr>
<tr>
<td class="dark" width="100"><b>Result:</b></td>
<td class="border" id="sw_res_2"> </td>
</tr>
<tr>
<td class="dark"><b>P-value:</b></td>
<td class="border" id="sw_p_2"> </td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="viz">
<div class="label16">
<h3 class="f16"> Data Visualization</h3>
</div>
<div id="chart"></div>
</div>
</div>
</div>
</body>
</html>