Say you’ve had an experiment that produced some surprising results, so you replicated it with a new experiment. Or say you’ve got a number of separate experiments for multiple channels, yielding different reports from the same hypothesis. In the past, this would have potentially provided underpowered experiment reports without sound evidence. But there’s a more powerful way to increase our ability to measure a signal.
At eBay, experimenters often collect several rounds of samples for the same hypothesis in an A/B test, or replicate an experiment that had surprising results. Sometimes, experimenters might also conduct separate experiments for each site or channel, which produces different reports of the same hypothesis. Say experimenters want to measure the effect of a new design to be rolled out on all eBay sites. To speed up the decision, they don’t have to wait until the new design is implemented on all 20+ eBay sites if it can not be done all at once in one sprint. Instead, they can gradually execute separate experiments site by site and demand a combined report at the end. The separate setup is often due to engineering considerations and curious Analysts/Product Manager about the combined readout.
In this article, we discuss a powerful metaanalysis method, weighted ztest, which can combine readouts (including pvalues, lift, CI, etc) from multiple independent experiments for the same hypothesis. As discussed by Dmitri Zaykin, a biostatistician at NIH, it effectively increases power and, thus, the ability to measure a signal. Many industrial practitioners often consider Fisher's metaanalysis, as mentioned by Kohavi, an influencer in A/B testing. However, Fisher's metaanalysis can only work for onesided tests. In eBay, we applied the weighted ztest carefully and would like to share our practices with other early adopters.
The weighted ztest brings several benefits at zero cost. By leveraging all the collected samples in different experiments, the ztest makes the combined readout more powerful, producing the effectiveness of a much larger and more robust experiment. If you have underpowered experiments owing to factors like a small sample size, you can combine them to produce higher statistical power, smaller confidence intervals (CI) and fewer false positives. This combined pvalue can be used to support a hypothesis tested in several experiments, so a series of nonsignificant results may now collectively suggest significance.
Weighted ZTest
Let’s look at some of the technical details.^{1}
To understand how to combine pvalues, first assume that we observe k experiments with pvalue p_{i} and zstatistic Z_{i} for the ith experiments. The weighted ztest also utilizes an experimentspecific weight, w_{i}, for each experiment. As discussed by Dmitri Zaykin, we use these different weights to combine independent tests to maximize the power.
For the k experiments, the weighted ztest twosided pvalue is
where Z_{i} is the zstatistic of the ith experiment.
Lipták suggested that the weights in this method "should be chosen proportional to the ‘expected’ difference between the null hypothesis and the real situation and inversely proportional to the standard deviation of the statistic used in the ith experiment."^{2}
Because heterogeneity may affect the variance, we use the standard deviation of each experiment, $SE_i$, to weight each ztest statistic:
Where $SE_i = \sqrt{\frac{S_{iT}^2}{n_{iT}} + \frac{S_{iC}^2}{n_{iC}}}$ (note that $S_{iT}$ and $S_{iC}$ are the standard deviation of ith experiment’s treatment and control groups.)
Increasing more power
In addition, we can use a simple baseline method which pools all the samples in each experiment together and computes the zstatistic and pvalue.
The simulations below compare the power between the baseline and weighted ztest. In particular, we consider three situations with heterogeneity effects:

Randomly selected mean for the two experiments, but same unit variance

Randomly selected variance for the two experiments, but same mean value

Randomly selected both mean and variance for the two experiments
Below, you can see that the weighted ztest achieves better power without any deterioration of TypeI error, especially when there exist heterogeneity effects on variance.
Applying in eBay
To exploit the power of the weighted ztest, these experiments we want to combine can be executed orthogonally (for example, in different planes or swim lanes) or mutually exclusively (on different sites or channels). All we needed are two assumptions, which are frequently satisfied:

Test the same hypothesis for all experiments (e.g., same variants)

Combine independent statistical tests with each other
In eBay, specifically, the two assumptions are formed into six checks:

The data collection of experiments must be completed

No quality issues that avoid best practice (e.g., more than one week, no sample delta, etc)

Equal traffic allocation ratio between treatment and control

No shared control for any two experiments

No more than a one week difference between the experiment durations

No more than a three month difference between experiment start dates
When an experimenter requests to combine several experiments, Touchstone (eBay’s experimentation platform) automatically runs the checks. After passing all six checks, the experimenter is ready to combine readouts to increase the statistical power. Below, we illustrate the procedures with an eBay example.
An eBay Example
Consider the case where experimenters want to combine two reports, Experiment A and Experiment B, which both pass our assumption checks. The domain team wants to measure the impact on the shopping experience if they recommend items to users without deduplicating based on previously viewed or clicked items, so the treatment variant is to remove the deduplicating rules for all the placements on the View Item page. They implement the treatment for the eBay website and native app separately, so the experimenter collects samples through two different experiments (one is for the eBay website, the other is for the native app).
Below, we provide an example of how to combine results for the metric M.
In the each individual report, we have:
Step 1: Compute standard error per experiment using $SE_i = \sqrt{\frac{S_{iT}^2}{n_{iT}} + \frac{S_{iC}^2}{n_{iC}}}$, where $S_{iT}$ and $S_{iC}$ are the standard deviations of ith experiment’s treatment and control groups, respectively.
Step 2: Compute the weights $w_i = 1\mathop{/}SE_{i}$ and normalized weight $\phi_i = \frac{w_i^2}{\sum_{i=1}^k{w_i^2}}$ that satisfy the property $\sum_{i=1}^{k}{\phi_i}=1$, which helps provide an interpretable combination.
Step 3: Compute combined zstatistics: $Z_{combined}=\frac{\sum_{i=1}^k{w_iZ_i}}{\sqrt{\sum_{i=1}^k{w_i^2}}}$
Step 4: Compute combined pvalue: $p_{combined}=2\times[1\Phi(\leftZ_{combined}\right)]$
Step 5: compute Combined Lift, CI and means.

$\mu_{T} = \sum_{i=1}^{k}{\phi_i \mu_{iT}}$ $\mu_{C} = \sum_{i=1}^{k}{\phi_i \mu_{iC}}$ $\Delta_{combined} = \mu_{T}  \mu_{C}$
 $SE_{combined}=\sqrt{\sum_{i=1}^k\phi_i^2SE_i^2}$
By combining experiments, we shrink the confidence interval, CI: the combination is more sensitive than each of the original experiments.
Conclusion
In eBay, we leverage the weighted ztestin Touchstone (eBay’s experimentation platform) and provide experimenters an option to compute a combined test statistic when experimenters have several independent experiments with the same hypothesis. It effectively combines all the collected samples, provides more power and increases the ability to measure weak signals. The option is now available for product managers and analysts within eBay.
References
1. Advanced readers can check Dmitri Zaykin’s paper for a more indepth discussion.
2. Lipták, T. (1958). On the combination of independent tests.