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    <p>Best approach, no crazed values, <a href="https://stackoverflow.com/questions/609501/generating-a-random-decimal-in-c/610228#610228">distributed with respect to the representable intervals on the floating-point number line</a> (removed "uniform" as with respect to a continuous number line it is decidedly non-uniform):</p> <pre><code>static float NextFloat(Random random) { double mantissa = (random.NextDouble() * 2.0) - 1.0; double exponent = Math.Pow(2.0, random.Next(-126, 128)); return (float)(mantissa * exponent); } </code></pre> <p>Another approach which will give you some crazed values (uniform distribution of bit patterns), potentially useful for fuzzing:</p> <pre><code>static float NextFloat(Random random) { var buffer = new byte[4]; random.NextBytes(buffer); return BitConverter.ToSingle(buffer,0); } </code></pre> <p>Least useful approach:</p> <pre><code>static float NextFloat(Random random) { // Not a uniform distribution w.r.t. the binary floating-point number line // which makes sense given that NextDouble is uniform from 0.0 to 1.0. // Uniform w.r.t. a continuous number line. // // The range produced by this method is 6.8e38. // // Therefore if NextDouble produces values in the range of 0.0 to 0.1 // 10% of the time, we will only produce numbers less than 1e38 about // 10% of the time, which does not make sense. var result = (random.NextDouble() * (Single.MaxValue - (double)Single.MinValue)) + Single.MinValue; return (float)result; } </code></pre> <p>Floating point number line from: <a href="http://www.randelshofer.ch/fhw/gri/float.html" rel="noreferrer">Intel Architecture Software Developer's Manual Volume 1: Basic Architecture.</a> The Y-axis is logarithmic (base-2) because consecutive binary floating point numbers do not differ linearly. </p> <p><img src="https://3.bp.blogspot.com/_XnHVrPnrx7o/TFHUXopxYNI/AAAAAAAABrs/2H_tT_VuUOk/s1600/distribution.png" alt="Comparison of distributions, logarithmic Y-axis"></p>
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    1. COThere doesn't seem to be a BitConverter.GetSingle method. Do you mean ToSingle?
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    2. COThat would be what I mean...
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    3. COUsing random bytes can easily end up with a NaN value, and the distribution may well be non-uniform. (I wouldn't like to predict the distribution.)
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