Evidence
Evidence is information, observations, or material used to support, challenge, or assess a claim, conclusion, event, or outcome.
Writing for Developers: Blogs that get read
Detailed observations, measurements, artifacts, and test results used to reconstruct the causes of a software failure.
- How it is used
- Supports technical claims and bug investigations by giving skeptical readers inspectable grounds for judgment.
- How deep it goes
- Defines evidence broadly, gives replacement patterns, and catalogs diagnostic artifacts without teaching their underlying tools or analysis.
- Examples
- Uses benchmarks, code samples, monitoring graphs, flame graphs, tracing data, attempted fixes, dependency graphs, and memory tests.
- What you can do after
- Diagnose/debug with it by collecting artifacts and testing arguable explanations against concrete results.
Read how Writing for Developers: Blogs that get read explains evidence →
Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and Terraform
Information that helps identify the location and cause of a software defect.
- How it is used
- Directs debugging toward the real defect location after triage and before isolation.
- How deep it goes
- Defines evidence, lists operational sources, and stops short of prescribing a fixed collection procedure.
- Examples
- Uses logs, error reports, request traces, user reports, Kubernetes tooling, call stacks, and code version metadata.
- What you can do after
- Diagnose/debug with it by narrowing investigation toward the real defect location.
Fabulous Adventures in Data Structures and Algorithms
Evidence is an observed fact or data value that changes the probability assigned to a hypothesis or event.
- How it is used
- Updates beliefs about causes after observing effects, supporting diagnosis and evidence-based decisions.
- How deep it goes
- Explains evidence through priors, likelihoods, joint distributions, filtering, projection, and posterior computation, including counterintuitive base rates.
- Examples
- Uses cold-and-sneezing survey data, a rare-disease medical test, and repeated coin-flip observations.
- What you can do after
- Apply Bayesian reasoning in probabilistic programs to compute posterior beliefs from priors, likelihoods, and observations.
Read how Fabulous Adventures in Data Structures and Algorithms explains evidence →
Effective Data Analysis
Evidence is information that supports or challenges a hypothesis, theory, or conclusion without making it irrevocable.
- How it is used
- Supports hypothesis evaluation while keeping conclusions open to revision in experiments, quasi-experiments, and stakeholder decisions.
- How deep it goes
- Defines evidence against proof and discusses subgroup comparisons, interaction effects, and Type 1 error risk without detailing analysis procedures.
- Examples
- Uses subscription renewal, tutoring outcomes, website engagement, subgroup trends, and quasi-experimental group assignment as examples.
- What you can do after
- Evaluate/understand claims without treating supporting data as irrevocable proof.
Related concepts
- dry runproduced by · A dry run is an execution mode that previews, validates, or reports an operation’s prospective behavior without carrying out the effects being previewed.
- monitoringproduced by · Monitoring is the ongoing observation and measurement of a system’s behavior, state, or operational signals to assess its condition and detect changes or problems.
- bayesian statisticsused by · Bayesian statistics is a framework for statistical inference that combines prior probability distributions with observed-data likelihoods to update probabilities over hypotheses or unknown quantities.
- corepart of · A blog-post optimization group concerned with focus, logical organization, and adequate technical support.
- software artifactincludes · A software artifact is a persistent work product or object produced or maintained during software development or a related workflow.
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