{"id":6962,"date":"2026-03-23T19:20:08","date_gmt":"2026-03-23T19:20:08","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/survivorship-bias\/"},"modified":"2026-03-23T19:20:08","modified_gmt":"2026-03-23T19:20:08","slug":"survivorship-bias","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/survivorship-bias\/","title":{"rendered":"Survivorship Bias: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

Survivorship Bias is one of the most common (and most expensive) ways teams misread performance in Conversion & Measurement<\/strong>. It happens when you only analyze the campaigns, users, pages, or experiments that \u201csurvived\u201d long enough to be observed\u2014while missing the ones that failed, churned, were paused, or never got tracked correctly. In Analytics<\/strong>, that blind spot can make weak strategies look brilliant and strong strategies look risky.<\/p>\n\n\n\n

Modern marketing stacks create many opportunities for Survivorship Bias: attribution gaps, cookie loss, self-selected cohorts, incomplete event tracking, and reporting that defaults to \u201cavailable\u201d data rather than \u201crepresentative\u201d data. If your Conversion & Measurement<\/strong> strategy doesn\u2019t account for what\u2019s missing, your optimization loop will systematically reinforce the wrong decisions.<\/p>\n\n\n\n

What Is Survivorship Bias?<\/h2>\n\n\n\n

Survivorship Bias is a logical error where conclusions are drawn from a set of \u201cwinners\u201d or visible outcomes, while ignoring the invisible set of \u201closers\u201d or excluded cases. In marketing, that often means analyzing only converting users, active accounts, high-performing creatives, or successful A\/B tests\u2014without equally considering non-converters, churned users, rejected ads, paused campaigns, or failed experiments.<\/p>\n\n\n\n

The core concept is simple: the data you can see is not always the data you should generalize from<\/strong>. Survivorship Bias creeps in when the observation process filters reality.<\/p>\n\n\n\n

In business terms, Survivorship Bias causes overconfidence. Teams attribute success to tactics that merely correlate with survival (e.g., \u201cour best customers all attended webinars\u201d) rather than tactics that caused success (e.g., \u201cwebinars increased conversion among the broader audience\u201d).<\/p>\n\n\n\n

Within Conversion & Measurement<\/strong>, Survivorship Bias shows up in funnel analysis, cohort reporting, experiment readouts, and attribution. Inside Analytics<\/strong>, it affects how data is collected, which records are retained, and which segments are considered \u201cprimary\u201d in dashboards and KPIs.<\/p>\n\n\n\n

Why Survivorship Bias Matters in Conversion & Measurement<\/h2>\n\n\n\n

Survivorship Bias matters because it directly impacts decisions about budget, targeting, messaging, product changes, and channel strategy. When you optimize based on surviving outcomes, you often:<\/p>\n\n\n\n

    \n
  • Overinvest in channels that look efficient only because failures were filtered out<\/li>\n
  • Underestimate acquisition costs by ignoring drop-offs or untracked users<\/li>\n
  • Misjudge lifecycle performance by analyzing only retained customers<\/li>\n
  • \u201cProve\u201d hypotheses by selecting data that had to succeed to be counted<\/li>\n<\/ul>\n\n\n\n

    The business value of addressing Survivorship Bias is better forecasting and more stable growth. In Conversion & Measurement<\/strong>, your job isn\u2019t just to improve the numbers\u2014it\u2019s to improve the truthfulness<\/em> of what the numbers represent. Accurate Analytics<\/strong> creates competitive advantage because it prevents strategy from being driven by comforting, biased narratives.<\/p>\n\n\n\n

    How Survivorship Bias Works<\/h2>\n\n\n\n

    Survivorship Bias is conceptual, but it has a repeatable pattern in real marketing workflows:<\/p>\n\n\n\n

      \n
    1. \n

      Trigger: a filtered observation process<\/strong>\n Data gets filtered by design (e.g., only \u201cactive users\u201d) or by accident (e.g., tracking fails on certain devices). As a result, only a subset is measurable.<\/p>\n<\/li>\n

    2. \n

      Processing: analysis happens on the visible subset<\/strong>\n Dashboards, attribution reports, and experiment results are calculated from the surviving records. The missing cases are rarely quantified.<\/p>\n<\/li>\n

    3. \n

      Application: decisions are made based on biased evidence<\/strong>\n Budgets shift, bids change, landing pages are redesigned, and product roadmaps evolve\u2014based on insights that may not apply to the broader population.<\/p>\n<\/li>\n

    4. \n

      Outcome: the system reinforces the bias<\/strong>\n Teams keep feeding resources into what looks like success. Over time, Survivorship Bias can become \u201cstrategy,\u201d not just a measurement error.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      In Conversion & Measurement<\/strong>, this is particularly dangerous because optimization is iterative. A small bias compounded across weekly decisions can produce large opportunity costs.<\/p>\n\n\n\n

      Key Components of Survivorship Bias<\/h2>\n\n\n\n

      Survivorship Bias isn\u2019t a single bug; it emerges from multiple components in your measurement ecosystem:<\/p>\n\n\n\n

      Data inputs that commonly exclude \u201cnon-survivors\u201d<\/h3>\n\n\n\n
        \n
      • Ad impressions and clicks that never get attributed to outcomes due to identity loss<\/li>\n
      • Sessions from users who block scripts or decline consent<\/li>\n
      • Leads that never enter the CRM because of form errors or routing rules<\/li>\n
      • Trials that churn before onboarding events fire<\/li>\n
      • Campaigns paused early and excluded from retrospectives<\/li>\n<\/ul>\n\n\n\n

        Systems and processes that amplify the bias<\/h3>\n\n\n\n
          \n
        • Funnel reports that start at \u201cpage_view\u201d instead of \u201cad_exposure\u201d<\/li>\n
        • Attribution models that rely on trackable identifiers only<\/li>\n
        • Experiment analysis that excludes users who didn\u2019t complete a flow<\/li>\n
        • Dashboards built on \u201cactive users\u201d and \u201cqualified leads\u201d only<\/li>\n<\/ul>\n\n\n\n

          Governance and responsibilities<\/h3>\n\n\n\n
            \n
          • Clear ownership of event taxonomy and QA<\/li>\n
          • Policies for handling missing data (and disclosing it)<\/li>\n
          • Standard experiment rules (intent-to-treat vs. completers-only)<\/li>\n
          • Cross-team alignment between marketing ops, data, product, and sales<\/li>\n<\/ul>\n\n\n\n

            Good Analytics<\/strong> practice treats missingness as a first-class measurement problem, not a footnote.<\/p>\n\n\n\n

            Types of Survivorship Bias<\/h2>\n\n\n\n

            Survivorship Bias doesn\u2019t have one universally agreed taxonomy in marketing, but several practical \u201ccontexts\u201d come up repeatedly in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

            Cohort survivorship bias<\/h3>\n\n\n\n

            Analyzing only users who stayed long enough to show up in later-period metrics (e.g., \u201cDay-30 users convert better\u201d) while ignoring those who churned early.<\/p>\n\n\n\n

            Channel survivorship bias<\/h3>\n\n\n\n

            Evaluating only channels that can be reliably tracked end-to-end, which can make privacy-resilient channels look \u201cworse\u201d even if they drive real incrementality.<\/p>\n\n\n\n

            Experiment survivorship bias<\/h3>\n\n\n\n

            Interpreting A\/B test results based on only those who completed the experience (e.g., excluding users who bounced), which often inflates perceived lift.<\/p>\n\n\n\n

            Creative and campaign survivorship bias<\/h3>\n\n\n\n

            Reviewing only the creatives or campaigns that ran long enough to generate statistically comforting results\u2014while early failures disappear from the learning set.<\/p>\n\n\n\n

            Each context changes where<\/em> the bias enters, but the fix is consistent: expand what you count, and quantify what you miss.<\/p>\n\n\n\n

            Real-World Examples of Survivorship Bias<\/h2>\n\n\n\n

            Example 1: \u201cOur best customers all use feature X\u201d<\/h3>\n\n\n\n

            A SaaS team notices that retained customers heavily use Feature X, so marketing shifts messaging to promote it. But they only analyzed retained users\u2014classic Survivorship Bias. In reality, many churned users attempted Feature X and failed due to onboarding friction. Proper Analytics<\/strong> would compare feature adoption for retained and<\/em> churned cohorts, then segment by time-to-first-value. In Conversion & Measurement<\/strong>, the actionable insight might be \u201cFeature X needs guided setup,\u201d not \u201cFeature X is the hook.\u201d<\/p>\n\n\n\n

            Example 2: A\/B test shows a huge conversion lift\u2014until you include bouncers<\/h3>\n\n\n\n

            A landing page test reports +18% conversion rate because the analysis includes only visitors who reached the form step. Visitors who bounced earlier weren\u2019t counted. That\u2019s Survivorship Bias introduced by a truncated funnel. In Conversion & Measurement<\/strong>, you\u2019d use an intent-to-treat approach: include everyone assigned to variant A or B from the first measurable exposure, then evaluate end conversions. Analytics<\/strong> instrumentation must support variant assignment at entry, not mid-funnel.<\/p>\n\n\n\n

            Example 3: ROAS looks great because only trackable purchases are counted<\/h3>\n\n\n\n

            A retailer runs ads across multiple platforms. Purchases from users with blocked tracking or cross-device journeys fail to attribute, so reported ROAS improves as privacy constraints increase. That\u2019s Survivorship Bias: only \u201csurviving\u201d attributable conversions are counted. Better Conversion & Measurement<\/strong> would incorporate modeled conversions, geo\/holdout tests, or blended MER-style evaluation to reduce dependence on trackable subsets. Analytics<\/strong> should also report attribution coverage so decision-makers see what percentage is missing.<\/p>\n\n\n\n

            Benefits of Using Survivorship Bias (Meaning: Accounting for It)<\/h2>\n\n\n\n

            You don\u2019t \u201cuse\u201d Survivorship Bias as a tactic\u2014you mitigate it. Teams that actively detect and reduce Survivorship Bias in Conversion & Measurement<\/strong> tend to see:<\/p>\n\n\n\n

              \n
            • More reliable optimization:<\/strong> Fewer whiplash decisions driven by misleading spikes<\/li>\n
            • Better budget allocation:<\/strong> Less overspending on channels that merely measure well<\/li>\n
            • Improved forecasting:<\/strong> Cleaner CAC, LTV, and payback estimates when missingness is quantified<\/li>\n
            • Higher operational efficiency:<\/strong> Fewer reworks caused by \u201cinsights\u201d that don\u2019t replicate<\/li>\n
            • Better customer experience:<\/strong> Fixing hidden drop-off points often improves journeys for everyone, not just converters<\/li>\n<\/ul>\n\n\n\n

              In Analytics<\/strong>, the biggest benefit is trust: stakeholders learn which numbers are directional and which are decision-grade.<\/p>\n\n\n\n

              Challenges of Survivorship Bias<\/h2>\n\n\n\n

              Survivorship Bias is easy to describe and hard to eliminate because it\u2019s often structural:<\/p>\n\n\n\n

                \n
              • Identity and privacy constraints:<\/strong> Consent choices, cookie loss, and platform limitations reduce observability, affecting Analytics<\/strong> completeness.<\/li>\n
              • Instrumentation gaps:<\/strong> If the first event in your funnel fires late, you can\u2019t properly evaluate drop-offs earlier in the journey.<\/li>\n
              • CRM and ops filtering:<\/strong> \u201cQualified\u201d stages can hide lead loss, disqualifications, and routing failures that matter for Conversion & Measurement<\/strong>.<\/li>\n
              • Selection effects:<\/strong> Users who engage more generate more data, which makes them overrepresented in analysis.<\/li>\n
              • Incentive misalignment:<\/strong> Teams prefer reports that show success; acknowledging missing data can feel politically risky.<\/li>\n<\/ul>\n\n\n\n

                The goal isn\u2019t perfect data\u2014it\u2019s honest measurement with known error bounds and clear coverage.<\/p>\n\n\n\n

                Best Practices for Survivorship Bias<\/h2>\n\n\n\n

                Design measurement to include the full population<\/h3>\n\n\n\n
                  \n
                • Track from the earliest feasible exposure (ad click, landing view, signup start), not just from mid-funnel steps.<\/li>\n
                • Use consistent identifiers and event schemas to reduce \u201csilent dropouts.\u201d<\/li>\n<\/ul>\n\n\n\n

                  Report what\u2019s missing, not just what\u2019s measured<\/h3>\n\n\n\n
                    \n
                  • Add attribution coverage, consent rates, and event-loss estimates to core Analytics<\/strong> dashboards.<\/li>\n
                  • Flag segments with low observability (e.g., certain browsers, regions, or devices).<\/li>\n<\/ul>\n\n\n\n

                    Use robust experiment and analysis methods<\/h3>\n\n\n\n
                      \n
                    • Prefer intent-to-treat analysis for tests tied to Conversion & Measurement<\/strong> outcomes.<\/li>\n
                    • Predefine exclusion rules; avoid post-hoc filtering that \u201ccleans\u201d away inconvenient users.<\/li>\n<\/ul>\n\n\n\n

                      Balance platform reports with independent views<\/h3>\n\n\n\n
                        \n
                      • Reconcile ad platform conversions with first-party outcomes and back-office truth (orders, revenue, refunds).<\/li>\n
                      • Use blended measurement approaches when attribution is incomplete.<\/li>\n<\/ul>\n\n\n\n

                        Institutionalize learning from failures<\/h3>\n\n\n\n
                          \n
                        • Store results from paused campaigns and failed creatives in a searchable repository.<\/li>\n
                        • Run post-mortems that include \u201cwhy it failed\u201d data, not just \u201cwhat worked.\u201d<\/li>\n<\/ul>\n\n\n\n

                          These practices reduce Survivorship Bias by making non-survivors visible enough to learn from.<\/p>\n\n\n\n

                          Tools Used for Survivorship Bias<\/h2>\n\n\n\n

                          Survivorship Bias is managed through measurement design and workflow discipline more than a single tool. Common tool categories in Conversion & Measurement<\/strong> and Analytics<\/strong> include:<\/p>\n\n\n\n

                            \n
                          • Analytics tools:<\/strong> Event-based and session-based analysis to inspect funnels, cohorts, and drop-offs; ability to view data quality and sampling behavior.<\/li>\n
                          • Tag management and instrumentation systems:<\/strong> Central control for event taxonomy, consent behavior, QA processes, and versioning\u2014critical for reducing silent measurement loss.<\/li>\n
                          • Data warehouses and transformation pipelines:<\/strong> Joining ad, web, product, and CRM data to see what gets excluded when relying on one source.<\/li>\n
                          • Experimentation platforms or frameworks:<\/strong> Proper randomization, assignment logging, and analysis methods to avoid experiment survivorship bias.<\/li>\n
                          • CRM systems and revenue ops tooling:<\/strong> Visibility into disqualifications, lead routing, stage transitions, and \u201clost reasons.\u201d<\/li>\n
                          • Reporting dashboards and BI layers:<\/strong> Standardized KPI definitions and data-quality annotations so stakeholders don\u2019t confuse partial visibility with truth.<\/li>\n<\/ul>\n\n\n\n

                            The most important \u201ctool\u201d is a repeatable measurement governance process that treats bias as an operational risk.<\/p>\n\n\n\n

                            Metrics Related to Survivorship Bias<\/h2>\n\n\n\n

                            You can\u2019t measure Survivorship Bias directly as a single KPI, but you can track indicators that reveal when it\u2019s likely distorting Analytics<\/strong> and Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                              \n
                            • Attribution coverage rate:<\/strong> Percentage of revenue\/conversions that can be tied to a source\/medium or campaign.<\/li>\n
                            • Event capture rate:<\/strong> Ratio of expected events to observed events (often estimated via server logs, reconciliation, or QA sampling).<\/li>\n
                            • Consent opt-in rate and impact:<\/strong> How consent choices shift observed conversion rates and audience composition.<\/li>\n
                            • Funnel entry completeness:<\/strong> Share of users counted at step 1 versus later steps (a sign your funnel starts too late).<\/li>\n
                            • Cohort representativeness checks:<\/strong> Comparing demographics, devices, regions, or acquisition sources between measured vs. unmeasured groups.<\/li>\n
                            • Drop-off and churn rates by segment:<\/strong> Especially for new users and low-engagement segments that often disappear from \u201csurvivor\u201d datasets.<\/li>\n
                            • Variance between platform-reported and first-party outcomes:<\/strong> Persistent gaps can signal survivorship-filtered reporting.<\/li>\n<\/ul>\n\n\n\n

                              Treat these as \u201cmeasurement health\u201d metrics alongside performance KPIs.<\/p>\n\n\n\n

                              Future Trends of Survivorship Bias<\/h2>\n\n\n\n

                              Several trends will make Survivorship Bias more important\u2014not less\u2014in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                \n
                              • AI-assisted optimization:<\/strong> Models trained on biased outcome data will confidently recommend the wrong actions. Better Analytics<\/strong> will require bias audits and representativeness checks before model deployment.<\/li>\n
                              • More automation in bidding and personalization:<\/strong> Automated systems react to observable conversions; if observability is uneven, automation amplifies Survivorship Bias.<\/li>\n
                              • Privacy-driven measurement shifts:<\/strong> As user-level tracking becomes less complete, teams will rely more on aggregation, modeling, and experiments to counter biased visibility.<\/li>\n
                              • Server-side and first-party measurement growth:<\/strong> Moving measurement closer to first-party systems can reduce some missingness, but it can also introduce new survivorship filters if implementations are incomplete.<\/li>\n
                              • Incrementality and causal methods becoming standard:<\/strong> Holdouts, geo tests, and uplift modeling help bypass the \u201conly attributed conversions survive\u201d problem.<\/li>\n<\/ul>\n\n\n\n

                                The future of Conversion & Measurement<\/strong> is less about perfect tracking and more about resilient, bias-aware Analytics<\/strong>.<\/p>\n\n\n\n

                                Survivorship Bias vs Related Terms<\/h2>\n\n\n\n

                                Survivorship Bias vs Selection Bias<\/h3>\n\n\n\n

                                Selection bias is broader: it occurs when your sample isn\u2019t representative of the population. Survivorship Bias is a specific form of selection bias where inclusion depends on \u201csurviving\u201d a process (remaining active, being trackable, completing steps). In Analytics<\/strong>, survivorship is often caused by drop-offs, churn, or tracking loss.<\/p>\n\n\n\n

                                Survivorship Bias vs Confirmation Bias<\/h3>\n\n\n\n

                                Confirmation bias is a human tendency to favor evidence that supports existing beliefs. Survivorship Bias can exist even with good intentions because the data pipeline filters outcomes. In practice, both can combine: teams may prefer survivor-only dashboards because they look better.<\/p>\n\n\n\n

                                Survivorship Bias vs Attribution Bias<\/h3>\n\n\n\n

                                Attribution bias (in marketing measurement) often refers to systematic mis-crediting of channels due to model limitations or tracking gaps. Survivorship Bias can be a root cause of attribution bias when only conversions with identifiable paths are counted, skewing Conversion & Measurement<\/strong> decisions.<\/p>\n\n\n\n

                                Who Should Learn Survivorship Bias<\/h2>\n\n\n\n
                                  \n
                                • Marketers:<\/strong> To avoid optimizing creative, targeting, and budgets based on only the visible winners.<\/li>\n
                                • Analysts:<\/strong> To build Analytics<\/strong> that communicates coverage, missingness, and uncertainty\u2014not just point estimates.<\/li>\n
                                • Agencies:<\/strong> To produce client reporting that withstands scrutiny and explains why \u201cmeasurable\u201d doesn\u2019t always mean \u201ceffective.\u201d<\/li>\n
                                • Business owners and founders:<\/strong> To make capital allocation decisions with realistic CAC\/LTV and a clear view of what data excludes.<\/li>\n
                                • Developers and data engineers:<\/strong> To instrument systems that minimize silent data loss and support robust Conversion & Measurement<\/strong> analysis.<\/li>\n<\/ul>\n\n\n\n

                                  Survivorship Bias is a shared responsibility: measurement design, data collection, and interpretation all contribute.<\/p>\n\n\n\n

                                  Summary of Survivorship Bias<\/h2>\n\n\n\n

                                  Survivorship Bias occurs when you draw conclusions from the outcomes you can observe while ignoring the outcomes filtered out by churn, drop-off, tracking loss, or process rules. It matters because it can make weak tactics look strong and lead to costly misallocation of spend and effort.<\/p>\n\n\n\n

                                  In Conversion & Measurement<\/strong>, Survivorship Bias affects funnels, cohorts, attribution, and experimentation\u2014especially when analysis starts too late or excludes non-completers. Strong Analytics<\/strong> reduces the risk by measuring earlier, reporting coverage and missingness, and using methods that reflect the full assigned population.<\/p>\n\n\n\n

                                  Frequently Asked Questions (FAQ)<\/h2>\n\n\n\n

                                  1) What is Survivorship Bias in marketing measurement?<\/h3>\n\n\n\n

                                  Survivorship Bias in marketing measurement is when you analyze only the users, campaigns, or conversions that remain visible in your data and ignore those that dropped out, churned, or weren\u2019t tracked\u2014leading to overly positive or distorted conclusions.<\/p>\n\n\n\n

                                  2) How can Survivorship Bias distort conversion rate optimization?<\/h3>\n\n\n\n

                                  It can inflate results if you only evaluate people who reached later funnel steps (like form views) and exclude early bounces. In Conversion & Measurement<\/strong>, that makes changes look more effective than they are for total traffic.<\/p>\n\n\n\n

                                  3) What\u2019s a simple way to detect Survivorship Bias in Analytics?<\/h3>\n\n\n\n

                                  Add coverage indicators: track how many sessions\/users are missing key events, how consent affects visibility, and what percentage of revenue is unattributed. In Analytics<\/strong>, a big gap between \u201cobserved\u201d and \u201cexpected\u201d is a warning sign.<\/p>\n\n\n\n

                                  4) Is Survivorship Bias the same as excluding outliers?<\/h3>\n\n\n\n

                                  No. Excluding outliers aims to reduce distortion from extreme values (and should be rule-based and justified). Survivorship Bias happens when the data you keep depends on survival or visibility, which systematically changes what your dataset represents.<\/p>\n\n\n\n

                                  5) How does Survivorship Bias affect attribution?<\/h3>\n\n\n\n

                                  Attribution often credits only conversions that can be linked to trackable journeys. If untrackable conversions are excluded, you end up optimizing toward channels that \u201csurvive\u201d measurement rather than those that truly drive incrementality in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                  6) What analysis method helps reduce Survivorship Bias in experiments?<\/h3>\n\n\n\n

                                  Intent-to-treat analysis: evaluate outcomes for everyone assigned to each variant from the first assignment point, not just those who completed the flow. This keeps drop-offs inside the measurement rather than filtering them out.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  Survivorship Bias is one of the most common (and most expensive) ways teams misread performance in **Conversion & Measurement**. It happens when you only analyze the campaigns, users, pages, or experiments that \u201csurvived\u201d long enough to be observed\u2014while missing the ones that failed, churned, were paused, or never got tracked correctly. In **Analytics**, that blind spot can make weak strategies look brilliant and strong strategies look risky.<\/p>\n","protected":false},"author":10235,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1887],"tags":[],"class_list":["post-6962","post","type-post","status-publish","format-standard","hentry","category-analytics"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6962","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/users\/10235"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/comments?post=6962"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6962\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6962"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6962"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6962"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}