Majaribio ya A/B kwa Maswali ya Miundo ya ML

Majibu na maelezo huonekana unapojibu maswali.

1. Kwa nini timu za A/B hufanya majaribio ya miundo katika toleo la umma badala ya kuamini vipimo vya nje ya mtandao?
   • Muundo ulio na usahihi bora zaidi wa nje ya mtandao bado unaweza kudhuru vipimo halisi vya biashara
   • Vipimo vya nje ya mtandao ni haramu
   • Uzalishaji ni wa bei nafuu kuliko majaribio ya nje ya mtandao
   • Data ya nje ya mtandao haipo
2. Je, kazi ya nasibu ina jukumu gani katika jaribio la A/B?
   • Inafanya mtihani kukimbia haraka
   • It supports balance between groups in expectation, subject to design and implementation assumptions
   • Inafuta watumiaji wenye upendeleo
   • Inapunguza idadi ya mifano inayohitajika
3. In a model A/B test, what usually happens in the treatment group?
   • The candidate scores requests but all outputs are withheld
   • Every user is assigned to both models for the same decision
   • The candidate model serves user-facing decisions for the randomly assigned group
   • The control model is removed before any comparison
4. Under a null hypothesis and stated test assumptions, how is a p-value interpreted?
   • As the probability that the null hypothesis is true
   • As the probability that the treatment caused the observed result
   • As the probability that the experiment will replicate
   • As the probability of data at least this extreme if the null hypothesis holds
5. How can a multi-armed bandit change an experiment while it runs?
   • It can allocate more traffic to variants with better observed outcomes and less to others
   • It keeps every variant at a fixed equal allocation throughout
   • It removes randomization from every assignment
   • It guarantees the best variant is identified without uncertainty
6. Madhumuni ya uchanganuzi wa nguvu kabla ya jaribio la A/B ni nini?
   • Ili kuchagua nembo ya jaribio
   • Ili kukadiria ni watumiaji wangapi wanaohitajika ili kugundua athari halisi
   • Ili kusimba trafiki kwa njia fiche
   • Ili kufuta kikundi cha kudhibiti
7. What does a sample-ratio mismatch (SRM) signal during an A/B test?
   • The treatment model is necessarily better
   • The experiment has sufficient statistical power
   • Observed assignment counts differ materially from the configured allocation, which can indicate a data or randomization problem
   • The primary metric improved causally
8. Why include guardrail metrics alongside an experiment’s primary success metric?
   • To guarantee the candidate wins on every metric
   • To replace the primary metric after results are observed
   • To avoid defining a decision rule before the test
   • To detect meaningful harms in other outcomes even when the primary metric improves