| # Copyright 2015 The Chromium Authors. All rights reserved. |
| # Use of this source code is governed by a BSD-style license that can be |
| # found in the LICENSE file. |
| |
| """Functions for doing independent two-sample t-tests and looking up p-values. |
| |
| > A t-test is any statistical hypothesis test in which the test statistic |
| > follows a Student's t distribution if the null hypothesis is supported. |
| > It can be used to determine if two sets of data are significantly different |
| > from each other. |
| |
| There are several conditions that the data under test should meet in order |
| for a t-test to be completely applicable: |
| - The data should be roughly normal in distribution. |
| - The two samples that are compared should be roughly similar in size. |
| |
| If these conditions cannot be met, then a non-parametric test may be more |
| appropriate (e.g. Mann-Whitney U test, K-S test or Anderson-Darley test). |
| |
| References: |
| http://en.wikipedia.org/wiki/Student%27s_t-test |
| http://en.wikipedia.org/wiki/Welch%27s_t-test |
| https://github.com/scipy/scipy/blob/master/scipy/stats/stats.py#L3244 |
| """ |
| |
| import bisect |
| import collections |
| import math |
| |
| from dashboard import math_utils |
| |
| |
| # A container for the results of a t-test. |
| TTestResult = collections.namedtuple('TTestResult', ('t', 'df', 'p')) |
| |
| |
| def WelchsTTest(sample1, sample2): |
| """Performs Welch's t-test on the two samples. |
| |
| Welch's t-test is an adaptation of Student's t-test which is used when the |
| two samples may have unequal variances. It is also an independent two-sample |
| t-test. |
| |
| Args: |
| sample1: A collection of numbers. |
| sample2: Another collection of numbers. |
| |
| Returns: |
| A 3-tuple (t-statistic, degrees of freedom, p-value). |
| |
| Raises: |
| RuntimeError: Invalid input. |
| """ |
| if not sample1: |
| raise RuntimeError('Empty sample 1: %s' % list(sample1)) |
| if not sample2: |
| raise RuntimeError('Empty sample 2: %s' % list(sample2)) |
| |
| stats1 = _MakeSampleStats(sample1) |
| stats2 = _MakeSampleStats(sample2) |
| t = _TValue(stats1, stats2) |
| df = _DegreesOfFreedom(stats1, stats2) |
| p = _LookupPValue(t, df) |
| return TTestResult(t, df, p) |
| |
| |
| # A SampleStats object contains pre-calculated stats about a sample. |
| SampleStats = collections.namedtuple('SampleStats', ('mean', 'var', 'size')) |
| |
| |
| def _MakeSampleStats(sample): |
| """Calculates relevant stats for a sample and makes a SampleStats object.""" |
| return SampleStats( |
| math_utils.Mean(sample), math_utils.Variance(sample), len(sample)) |
| |
| |
| def _TValue(stats1, stats2): |
| """Calculates a t-statistic value using the formula for Welch's t-test. |
| |
| The t value can be thought of as a signal-to-noise ratio; a higher t-value |
| tells you that the groups are more different. |
| |
| Args: |
| stats1: An SampleStats named tuple for the first sample. |
| stats2: An SampleStats named tuple for the second sample. |
| |
| Returns: |
| A t value, which may be negative or positive. |
| """ |
| # If variance of both segments is zero, then a very high t-value should |
| # be returned because any difference between the two samples could be |
| # considered a very clear difference. Also, in the equation, as the |
| # variance approaches zero, the quotient approaches infinity. |
| if stats1.var == 0 and stats2.var == 0: |
| return float('inf') |
| return math_utils.Divide( |
| stats1.mean - stats2.mean, |
| math.sqrt(stats1.var / stats1.size + |
| stats2.var / stats2.size)) |
| |
| |
| def _DegreesOfFreedom(stats1, stats2): |
| """Calculates degrees of freedom using the Welch-Satterthwaite formula. |
| |
| Degrees of freedom is a measure of sample size. For other types of tests, |
| degrees of freedom is sometimes N - 1, where N is the sample size. However, |
| for the Welch's t-test, the degrees of freedom is approximated with the |
| "Welch-Satterthwaite equation". |
| |
| The degrees of freedom returned from this function should be at least 1.0 |
| because the first row in the t-table is for degrees of freedom of 1.0. |
| |
| Args: |
| stats1: An SampleStats named tuple for the first sample. |
| stats2: An SampleStats named tuple for the second sample. |
| |
| Returns: |
| An estimate of degrees of freedom. Guaranteed to be at least 1.0. |
| |
| Raises: |
| RuntimeError: Invalid input. |
| """ |
| # When there's no variance in either sample, return 1. |
| if stats1.var == 0 and stats2.var == 0: |
| return 1.0 |
| if stats1.size < 2: |
| raise RuntimeError('Sample 1 size < 2. Actual size: %s' % stats1.size) |
| if stats2.size < 2: |
| raise RuntimeError('Sample 2 size < 2. Actual size: %s' % stats2.size) |
| df = math_utils.Divide( |
| (stats1.var / stats1.size + stats2.var / stats2.size) ** 2, |
| math_utils.Divide(stats1.var ** 2, |
| (stats1.size ** 2) * (stats1.size - 1)) + |
| math_utils.Divide(stats2.var ** 2, |
| (stats2.size ** 2) * (stats2.size - 1))) |
| return max(1.0, df) |
| |
| |
| # Below is a hard-coded table for looking up p-values. |
| # |
| # Normally, p-values are calculated based on the t-distribution formula. |
| # Looking up pre-calculated values is a less accurate but less complicated |
| # alternative. |
| # |
| # Reference: http://www.medcalc.org/manual/t-distribution.php |
| |
| # A list of p-values for a two-tailed test. The entries correspond to |
| # entries in the rows of the table below. |
| _TWO_TAIL = [1, 0.20, 0.10, 0.05, 0.02, 0.01, 0.005, 0.002, 0.001] |
| |
| # A map of degrees of freedom to lists of t-values. The index of the t-value |
| # can be used to look up the corresponding p-value. |
| _TABLE = [ |
| (1, [0, 3.078, 6.314, 12.706, 31.820, 63.657, 127.321, 318.309, 636.619]), |
| (2, [0, 1.886, 2.920, 4.303, 6.965, 9.925, 14.089, 22.327, 31.599]), |
| (3, [0, 1.638, 2.353, 3.182, 4.541, 5.841, 7.453, 10.215, 12.924]), |
| (4, [0, 1.533, 2.132, 2.776, 3.747, 4.604, 5.598, 7.173, 8.610]), |
| (5, [0, 1.476, 2.015, 2.571, 3.365, 4.032, 4.773, 5.893, 6.869]), |
| (6, [0, 1.440, 1.943, 2.447, 3.143, 3.707, 4.317, 5.208, 5.959]), |
| (7, [0, 1.415, 1.895, 2.365, 2.998, 3.499, 4.029, 4.785, 5.408]), |
| (8, [0, 1.397, 1.860, 2.306, 2.897, 3.355, 3.833, 4.501, 5.041]), |
| (9, [0, 1.383, 1.833, 2.262, 2.821, 3.250, 3.690, 4.297, 4.781]), |
| (10, [0, 1.372, 1.812, 2.228, 2.764, 3.169, 3.581, 4.144, 4.587]), |
| (11, [0, 1.363, 1.796, 2.201, 2.718, 3.106, 3.497, 4.025, 4.437]), |
| (12, [0, 1.356, 1.782, 2.179, 2.681, 3.055, 3.428, 3.930, 4.318]), |
| (13, [0, 1.350, 1.771, 2.160, 2.650, 3.012, 3.372, 3.852, 4.221]), |
| (14, [0, 1.345, 1.761, 2.145, 2.625, 2.977, 3.326, 3.787, 4.140]), |
| (15, [0, 1.341, 1.753, 2.131, 2.602, 2.947, 3.286, 3.733, 4.073]), |
| (16, [0, 1.337, 1.746, 2.120, 2.584, 2.921, 3.252, 3.686, 4.015]), |
| (17, [0, 1.333, 1.740, 2.110, 2.567, 2.898, 3.222, 3.646, 3.965]), |
| (18, [0, 1.330, 1.734, 2.101, 2.552, 2.878, 3.197, 3.610, 3.922]), |
| (19, [0, 1.328, 1.729, 2.093, 2.539, 2.861, 3.174, 3.579, 3.883]), |
| (20, [0, 1.325, 1.725, 2.086, 2.528, 2.845, 3.153, 3.552, 3.850]), |
| (21, [0, 1.323, 1.721, 2.080, 2.518, 2.831, 3.135, 3.527, 3.819]), |
| (22, [0, 1.321, 1.717, 2.074, 2.508, 2.819, 3.119, 3.505, 3.792]), |
| (23, [0, 1.319, 1.714, 2.069, 2.500, 2.807, 3.104, 3.485, 3.768]), |
| (24, [0, 1.318, 1.711, 2.064, 2.492, 2.797, 3.090, 3.467, 3.745]), |
| (25, [0, 1.316, 1.708, 2.060, 2.485, 2.787, 3.078, 3.450, 3.725]), |
| (26, [0, 1.315, 1.706, 2.056, 2.479, 2.779, 3.067, 3.435, 3.707]), |
| (27, [0, 1.314, 1.703, 2.052, 2.473, 2.771, 3.057, 3.421, 3.690]), |
| (28, [0, 1.313, 1.701, 2.048, 2.467, 2.763, 3.047, 3.408, 3.674]), |
| (29, [0, 1.311, 1.699, 2.045, 2.462, 2.756, 3.038, 3.396, 3.659]), |
| (30, [0, 1.310, 1.697, 2.042, 2.457, 2.750, 3.030, 3.385, 3.646]), |
| (31, [0, 1.309, 1.695, 2.040, 2.453, 2.744, 3.022, 3.375, 3.633]), |
| (32, [0, 1.309, 1.694, 2.037, 2.449, 2.738, 3.015, 3.365, 3.622]), |
| (33, [0, 1.308, 1.692, 2.035, 2.445, 2.733, 3.008, 3.356, 3.611]), |
| (34, [0, 1.307, 1.691, 2.032, 2.441, 2.728, 3.002, 3.348, 3.601]), |
| (35, [0, 1.306, 1.690, 2.030, 2.438, 2.724, 2.996, 3.340, 3.591]), |
| (36, [0, 1.306, 1.688, 2.028, 2.434, 2.719, 2.991, 3.333, 3.582]), |
| (37, [0, 1.305, 1.687, 2.026, 2.431, 2.715, 2.985, 3.326, 3.574]), |
| (38, [0, 1.304, 1.686, 2.024, 2.429, 2.712, 2.980, 3.319, 3.566]), |
| (39, [0, 1.304, 1.685, 2.023, 2.426, 2.708, 2.976, 3.313, 3.558]), |
| (40, [0, 1.303, 1.684, 2.021, 2.423, 2.704, 2.971, 3.307, 3.551]), |
| (42, [0, 1.302, 1.682, 2.018, 2.418, 2.698, 2.963, 3.296, 3.538]), |
| (44, [0, 1.301, 1.680, 2.015, 2.414, 2.692, 2.956, 3.286, 3.526]), |
| (46, [0, 1.300, 1.679, 2.013, 2.410, 2.687, 2.949, 3.277, 3.515]), |
| (48, [0, 1.299, 1.677, 2.011, 2.407, 2.682, 2.943, 3.269, 3.505]), |
| (50, [0, 1.299, 1.676, 2.009, 2.403, 2.678, 2.937, 3.261, 3.496]), |
| (60, [0, 1.296, 1.671, 2.000, 2.390, 2.660, 2.915, 3.232, 3.460]), |
| (70, [0, 1.294, 1.667, 1.994, 2.381, 2.648, 2.899, 3.211, 3.435]), |
| (80, [0, 1.292, 1.664, 1.990, 2.374, 2.639, 2.887, 3.195, 3.416]), |
| (90, [0, 1.291, 1.662, 1.987, 2.369, 2.632, 2.878, 3.183, 3.402]), |
| (100, [0, 1.290, 1.660, 1.984, 2.364, 2.626, 2.871, 3.174, 3.391]), |
| (120, [0, 1.289, 1.658, 1.980, 2.358, 2.617, 2.860, 3.160, 3.373]), |
| (150, [0, 1.287, 1.655, 1.976, 2.351, 2.609, 2.849, 3.145, 3.357]), |
| (200, [0, 1.286, 1.652, 1.972, 2.345, 2.601, 2.839, 3.131, 3.340]), |
| (300, [0, 1.284, 1.650, 1.968, 2.339, 2.592, 2.828, 3.118, 3.323]), |
| (500, [0, 1.283, 1.648, 1.965, 2.334, 2.586, 2.820, 3.107, 3.310]), |
| ] |
| |
| |
| def _LookupPValue(t, df): |
| """Looks up a p-value in a t-distribution table. |
| |
| Args: |
| t: A t statistic value; the result of a t-test. The negative sign will be |
| ignored because this is a two-tail test. |
| df: Number of degrees of freedom. |
| |
| Returns: |
| A p-value, which represents the likelihood of obtaining a result at least |
| as extreme as the one observed just by chance (the null hypothesis). |
| """ |
| assert df >= 1.0, 'Degrees of freedom must at least 1.0.' |
| |
| # bisect.bisect will return the index at which (df + 1,) would be |
| # inserted in the table; we want the row at the index before that. |
| t_table_row = _TABLE[bisect.bisect(_TABLE, (df + 1,)) - 1][1] |
| |
| # In this line, bisect.bisect would return the index in the row |
| # where another t would be inserted. If the given t-value is between |
| # two entries in the row, we're getting the entry for the next-lowest |
| # t-value, so here we also subtract one from the result of bisect.bisect. |
| return _TWO_TAIL[bisect.bisect(t_table_row, abs(t)) - 1] |