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doc_page | Test Stats Aggregators |
Test Stats Aggregators
Incorporates test statistics related aggregators, including z-score and p-value. Please refer to https://www.paypal-engineering.com/2017/06/29/democratizing-experimentation-data-for-product-innovations/ for math background and details.
Make sure to include druid-stats
extension in order to use these aggregrators.
Z-Score for two sample ztests post aggregator
Please refer to https://www.isixsigma.com/tools-templates/hypothesis-testing/making-sense-two-proportions-test/ and http://www.ucs.louisiana.edu/~jcb0773/Berry_statbook/Berry_statbook_chpt6.pdf for more details.
z = (p1 - p2) / S.E. (assuming null hypothesis is true)
Please see below for p1 and p2. Please note S.E. stands for standard error where
S.E. = sqrt{ p1 * ( 1 - p1 )/n1 + p2 * (1 - p2)/n2) }
(p1 – p2) is the observed difference between two sample proportions.
zscore2sample post aggregator
zscore2sample
: calculate the z-score using two-sample z-test while converting binary variables (e.g. success or not) to continuous variables (e.g. conversion rate).
{
"type": "zscore2sample",
"name": "<output_name>",
"successCount1": <post_aggregator> success count of sample 1,
"sample1Size": <post_aggregaror> sample 1 size,
"successCount2": <post_aggregator> success count of sample 2,
"sample2Size" : <post_aggregator> sample 2 size
}
Please note the post aggregator will be converting binary variables to continuous variables for two population proportions. Specifically
p1 = (successCount1) / (sample size 1)
p2 = (successCount2) / (sample size 2)
pvalue2tailedZtest post aggregator
pvalue2tailedZtest
: calculate p-value of two-sided z-test from zscore- pvalue2tailedZtest(zscore) - the input is a z-score which can be calculated using the zscore2sample post aggregator
{
"type": "pvalue2tailedZtest",
"name": "<output_name>",
"zScore": <zscore post_aggregator>
}
Example Usage
In this example, we use zscore2sample post aggregator to calculate z-score, and then feed the z-score to pvalue2tailedZtest post aggregator to calculate p-value.
A JSON query example can be as follows:
{
...
"postAggregations" : {
"type" : "pvalue2tailedZtest",
"name" : "pvalue",
"zScore" :
{
"type" : "zscore2sample",
"name" : "zscore",
"successCount1" :
{ "type" : "constant",
"name" : "successCountFromPopulation1Sample",
"value" : 300
},
"sample1Size" :
{ "type" : "constant",
"name" : "sampleSizeOfPopulation1",
"value" : 500
},
"successCount2":
{ "type" : "constant",
"name" : "successCountFromPopulation2Sample",
"value" : 450
},
"sample2Size" :
{ "type" : "constant",
"name" : "sampleSizeOfPopulation2",
"value" : 600
}
}
}
}