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<div class="titlepage"><div><div><h4 class="title">
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<a name="math_toolkit.stat_tut.overview.parameters"></a><a class="link" href="parameters.html" title="Parameters can be calculated">Parameters
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can be calculated</a>
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</h4></div></div></div>
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<p>
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Sometimes it's the parameters that define the distribution that you need
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to find. Suppose, for example, you have conducted a Students-t test for
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equal means and the result is borderline. Maybe your two samples differ
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from each other, or maybe they don't; based on the result of the test you
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can't be sure. A legitimate question to ask then is "How many more
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measurements would I have to take before I would get an X% probability
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that the difference is real?" Parameter finders can answer questions
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like this, and are necessarily different for each distribution. They are
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implemented as static member functions of the distributions, for example:
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</p>
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<pre class="programlisting"><span class="identifier">students_t</span><span class="special">::</span><span class="identifier">find_degrees_of_freedom</span><span class="special">(</span>
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<span class="number">1.3</span><span class="special">,</span> <span class="comment">// difference from true mean to detect</span>
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<span class="number">0.05</span><span class="special">,</span> <span class="comment">// maximum risk of falsely rejecting the null-hypothesis.</span>
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<span class="number">0.1</span><span class="special">,</span> <span class="comment">// maximum risk of falsely failing to reject the null-hypothesis.</span>
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<span class="number">0.13</span><span class="special">);</span> <span class="comment">// sample standard deviation</span>
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</pre>
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<p>
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Returns the number of degrees of freedom required to obtain a 95% probability
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that the observed differences in means is not down to chance alone. In
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the case that a borderline Students-t test result was previously obtained,
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this can be used to estimate how large the sample size would have to become
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before the observed difference was considered significant. It assumes,
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of course, that the sample mean and standard deviation are invariant with
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sample size.
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<td align="left"></td>
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<td align="right"><div class="copyright-footer">Copyright © 2006-2019 Nikhar
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Agrawal, Anton Bikineev, Paul A. Bristow, Marco Guazzone, Christopher Kormanyos,
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Hubert Holin, Bruno Lalande, John Maddock, Jeremy Murphy, Matthew Pulver, Johan
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Råde, Gautam Sewani, Benjamin Sobotta, Nicholas Thompson, Thijs van den Berg,
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Daryle Walker and Xiaogang Zhang<p>
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Distributed under the Boost Software License, Version 1.0. (See accompanying
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file LICENSE_1_0.txt or copy at <a href="http://www.boost.org/LICENSE_1_0.txt" target="_top">http://www.boost.org/LICENSE_1_0.txt</a>)
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