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[7.x] Add Student's t-test aggregation support (elastic#54469)
Adds t_test metric aggregation that can perform paired and unpaired two-sample t-tests. In this PR support for filters in unpaired is still missing. It will be added in a follow-up PR. Relates to elastic#53692
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docs/reference/aggregations/metrics/t-test-aggregation.asciidoc
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[role="xpack"] | ||
[testenv="basic"] | ||
[[search-aggregations-metrics-ttest-aggregation]] | ||
=== TTest Aggregation | ||
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A `t_test` metrics aggregation that performs a statistical hypothesis test in which the test statistic follows a Student's t-distribution | ||
under the null hypothesis on numeric values extracted from the aggregated documents or generated by provided scripts. In practice, this | ||
will tell you if the difference between two population means are statistically significant and did not occur by chance alone. | ||
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==== Syntax | ||
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A `t_test` aggregation looks like this in isolation: | ||
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[source,js] | ||
-------------------------------------------------- | ||
{ | ||
"t_test": { | ||
"a": "value_before", | ||
"b": "value_after", | ||
"type": "paired" | ||
} | ||
} | ||
-------------------------------------------------- | ||
// NOTCONSOLE | ||
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Assuming that we have a record of node start up times before and after upgrade, let's look at a t-test to see if upgrade affected | ||
the node start up time in a meaningful way. | ||
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[source,console] | ||
-------------------------------------------------- | ||
GET node_upgrade/_search | ||
{ | ||
"size": 0, | ||
"aggs" : { | ||
"startup_time_ttest" : { | ||
"t_test" : { | ||
"a" : {"field": "startup_time_before"}, <1> | ||
"b" : {"field": "startup_time_after"}, <2> | ||
"type": "paired" <3> | ||
} | ||
} | ||
} | ||
} | ||
-------------------------------------------------- | ||
// TEST[setup:node_upgrade] | ||
<1> The field `startup_time_before` must be a numeric field | ||
<2> The field `startup_time_after` must be a numeric field | ||
<3> Since we have data from the same nodes, we are using paired t-test. | ||
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The response will return the p-value or probability value for the test. It is the probability of obtaining results at least as extreme as | ||
the result processed by the aggregation, assuming that the null hypothesis is correct (which means there is no difference between | ||
population means). Smaller p-value means the null hypothesis is more likely to be incorrect and population means are indeed different. | ||
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[source,console-result] | ||
-------------------------------------------------- | ||
{ | ||
... | ||
"aggregations": { | ||
"startup_time_ttest": { | ||
"value": 0.1914368843365979 <1> | ||
} | ||
} | ||
} | ||
-------------------------------------------------- | ||
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/] | ||
<1> The p-value. | ||
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==== T-Test Types | ||
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The `t_test` aggregation supports unpaired and paired two-sample t-tests. The type of the test can be specified using the `type` parameter: | ||
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`"type": "paired"`:: performs paired t-test | ||
`"type": "homoscedastic"`:: performs two-sample equal variance test | ||
`"type": "heteroscedastic"`:: performs two-sample unequal variance test (this is default) | ||
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==== Script | ||
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The `t_test` metric supports scripting. For example, if we need to adjust out load times for the before values, we could use | ||
a script to recalculate them on-the-fly: | ||
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[source,console] | ||
-------------------------------------------------- | ||
GET node_upgrade/_search | ||
{ | ||
"size": 0, | ||
"aggs" : { | ||
"startup_time_ttest" : { | ||
"t_test" : { | ||
"a": { | ||
"script" : { | ||
"lang": "painless", | ||
"source": "doc['startup_time_before'].value - params.adjustment", <1> | ||
"params" : { | ||
"adjustment" : 10 <2> | ||
} | ||
} | ||
}, | ||
"b": { | ||
"field": "startup_time_after" <3> | ||
}, | ||
"type": "paired" | ||
} | ||
} | ||
} | ||
} | ||
-------------------------------------------------- | ||
// TEST[setup:node_upgrade] | ||
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<1> The `field` parameter is replaced with a `script` parameter, which uses the | ||
script to generate values which percentiles are calculated on | ||
<2> Scripting supports parameterized input just like any other script | ||
<3> We can mix scripts and fields | ||
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