// 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 kLogNormalMean; extern const base::FeatureParam kLogNormalDelta; extern const base::FeatureParam 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_