1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071 |
- // Copyright 2021 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.
- #ifndef COMPONENTS_METRICS_METRICS_DATA_VALIDATION_H_
- #define COMPONENTS_METRICS_METRICS_DATA_VALIDATION_H_
- #include "base/feature_list.h"
- #include "base/metrics/field_trial_params.h"
- #include "base/time/time.h"
- // Features and functions in this file are necessary to set up artificial A / B
- // experiments that help us better assess the accuracy and power of our field
- // trial data. All code in this file should not have any impact on client's
- // experience.
- namespace metrics {
- // Only used for testing.
- namespace internal {
- extern const base::Feature kPseudoMetricsEffectFeature;
- } // namespace internal
- // Used to assess the reliability of field trial data by sending artificial
- // non-uniform data drawn from a log normal distribution.
- extern const base::Feature kNonUniformityValidationFeature;
- // The parameters for the log normal distribution. They refer to the default
- // mean, the delta that would be applied to the default mean (the actual mean
- // equals mean + log(1 + delta)) and the standard deviation of the distribution
- // that's being generated. These parameters are carefully calculated so that
- // ~0.01% of data drawn from the distribution would fall in the underflow bucket
- // and ~0.01% of data in the overflow bucket. And they also leave us enough
- // wiggle room to shift mean using delta in experiments without losing precision
- // badly because of data in the overflow bucket.
- //
- // The way we get these numbers are based on the following calculation:
- // u := the lower threshold for the overflow bucket (in this case, 10000).
- // l := the upper threshold for the smallest bucket (in this case, 1).
- // p := the probability that an observation will fall in the highest bucket (in
- // this case, 0.01%) and also the probability that an observation will fall in
- // the lowest bucket.
- //
- // mean = (log(u) + log(l)) / 2
- // sd = (log(u) - log(l)) / (2 * qnorm(1-p))
- //
- // At this point, experiments should only control the delta but not mean and
- // stdDev. Putting them in feature params so that we can configure them from the
- // server side if we want.
- extern const base::FeatureParam<double> kLogNormalMean;
- extern const base::FeatureParam<double> kLogNormalDelta;
- extern const base::FeatureParam<double> kLogNormalStdDev;
- // In order to assess if we're able to accurately detect a statistically
- // significant difference in our field trial data, we set up pseudo metrics for
- // some of our key metrics. Values of these pseudo metrics are the linear
- // transformation (ax + b) of real values (x). The multiplicative factor (a) and
- // additive factor (b) are controlled by field trial experiments.
- //
- // Returns the sample value for a pseudo metric given the |sample| from the real
- // metric and the assigned field trial group. The input type is double because
- // we don't want to lose precision before applying transformation.
- double GetPseudoMetricsSample(double sample);
- // Returns the TimeDelta for a pseudo metric given the |sample| from the real
- // metric and the assigned field trial group. The unit of the additive factor
- // (b) is milliseconds.
- base::TimeDelta GetPseudoMetricsSample(base::TimeDelta sample);
- } // namespace metrics
- #endif // COMPONENTS_METRICS_METRICS_DATA_VALIDATION_H_
|