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    <p>Your <code>QuickRandom</code> implementation hasn't really an uniform distribution. The frequencies are generally higher at the lower values while <code>Math.random()</code> has a more uniform distribution. Here's a <a href="http://sscce.org" rel="noreferrer">SSCCE</a> which shows that:</p> <pre><code>package com.stackoverflow.q14491966; import java.util.Arrays; public class Test { public static void main(String[] args) throws Exception { QuickRandom qr = new QuickRandom(); int[] frequencies = new int[10]; for (int i = 0; i &lt; 100000; i++) { frequencies[(int) (qr.random() * 10)]++; } printDistribution("QR", frequencies); frequencies = new int[10]; for (int i = 0; i &lt; 100000; i++) { frequencies[(int) (Math.random() * 10)]++; } printDistribution("MR", frequencies); } public static void printDistribution(String name, int[] frequencies) { System.out.printf("%n%s distribution |8000 |9000 |10000 |11000 |12000%n", name); for (int i = 0; i &lt; 10; i++) { char[] bar = " ".toCharArray(); // 50 chars. Arrays.fill(bar, 0, Math.max(0, Math.min(50, frequencies[i] / 100 - 80)), '#'); System.out.printf("0.%dxxx: %6d :%s%n", i, frequencies[i], new String(bar)); } } } </code></pre> <p>The average result looks like this:</p> <pre class="lang-none prettyprint-override"><code>QR distribution |8000 |9000 |10000 |11000 |12000 0.0xxx: 11376 :################################# 0.1xxx: 11178 :############################### 0.2xxx: 11312 :################################# 0.3xxx: 10809 :############################ 0.4xxx: 10242 :###################### 0.5xxx: 8860 :######## 0.6xxx: 9004 :########## 0.7xxx: 8987 :######### 0.8xxx: 9075 :########## 0.9xxx: 9157 :########### MR distribution |8000 |9000 |10000 |11000 |12000 0.0xxx: 10097 :#################### 0.1xxx: 9901 :################### 0.2xxx: 10018 :#################### 0.3xxx: 9956 :################### 0.4xxx: 9974 :################### 0.5xxx: 10007 :#################### 0.6xxx: 10136 :##################### 0.7xxx: 9937 :################### 0.8xxx: 10029 :#################### 0.9xxx: 9945 :################### </code></pre> <p>If you repeat the test, you'll see that the QR distribution varies heavily, depending on the initial seeds, while the MR distribution is stable. Sometimes it reaches the desired uniform distribution, but more than often it doesn't. Here's one of the more extreme examples, it's even beyond the borders of the graph:</p> <pre class="lang-none prettyprint-override"><code>QR distribution |8000 |9000 |10000 |11000 |12000 0.0xxx: 41788 :################################################## 0.1xxx: 17495 :################################################## 0.2xxx: 10285 :###################### 0.3xxx: 7273 : 0.4xxx: 5643 : 0.5xxx: 4608 : 0.6xxx: 3907 : 0.7xxx: 3350 : 0.8xxx: 2999 : 0.9xxx: 2652 : </code></pre>
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    1. POIs this a "good enough" random algorithm; why isn't it used if it's faster?
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    1. USMob
    1. USBalusC
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    1. POIs this a "good enough" random algorithm; why isn't it used if it's faster?
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    1. CO+1 for numerical data - although looking on raw numbers can be misleading as it does not mean that they have statistically significant difference.
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      1. USMaciej Piechotka
    2. COThese results vary heavily with the initial seeds passed to `QuickRandom`. Sometimes, it's close to uniform, sometimes it's _much_ worse than this.
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      1. USPetr Janeček
    3. COThis is enough to eyeball the non-randomness. 10,000 per bin implies an expected sqrt{variance} of 100, and these bins are repeatedly many times that.
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