{"id":7133,"date":"2026-03-24T01:30:42","date_gmt":"2026-03-24T01:30:42","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/experiment-hypothesis\/"},"modified":"2026-03-24T01:30:42","modified_gmt":"2026-03-24T01:30:42","slug":"experiment-hypothesis","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/experiment-hypothesis\/","title":{"rendered":"Experiment Hypothesis: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CRO"},"content":{"rendered":"\n

An Experiment Hypothesis<\/strong> is the statement that turns an optimization idea into a testable claim\u2014one you can validate with data rather than opinion. In Conversion & Measurement<\/strong>, it acts as the bridge between what you observe (drop-offs, low engagement, weak leads) and what you change (copy, UX, offers, targeting) with clear expectations for impact and how you\u2019ll measure it.<\/p>\n\n\n\n

In CRO<\/strong>, teams often have no shortage of ideas. What separates high-performing programs from random \u201cbutton-color testing\u201d is the discipline to define an Experiment Hypothesis<\/strong> before building variants and collecting results. Done well, it increases the quality of experiments, improves learning velocity, and protects your organization from misinterpreting noisy data as \u201cwins.\u201d<\/p>\n\n\n\n

What Is Experiment Hypothesis?<\/h2>\n\n\n\n

An Experiment Hypothesis<\/strong> is a specific, measurable prediction about how a change will affect user behavior and business outcomes. It typically connects:<\/p>\n\n\n\n

    \n
  • a user problem or opportunity<\/strong> (what\u2019s happening now),<\/li>\n
  • a proposed change<\/strong> (what you\u2019ll modify),<\/li>\n
  • an expected impact<\/strong> (what should improve and why),<\/li>\n
  • and a measurement plan<\/strong> (how you\u2019ll judge success).<\/li>\n<\/ul>\n\n\n\n

    The core concept is simple: you\u2019re not just testing a variation\u2014you\u2019re testing a belief about cause and effect. In business terms, an Experiment Hypothesis<\/strong> is a risk-managed investment thesis: \u201cIf we change X for audience Y, metric Z will improve because of reason R.\u201d<\/p>\n\n\n\n

    Within Conversion & Measurement<\/strong>, the hypothesis ensures that analysis, instrumentation, and decision criteria are defined up front. Inside CRO<\/strong>, it becomes the organizing unit for experiment prioritization, design, and post-test learning.<\/p>\n\n\n\n

    Why Experiment Hypothesis Matters in Conversion & Measurement<\/h2>\n\n\n\n

    A strong Experiment Hypothesis<\/strong> improves strategy because it forces clarity before execution. Instead of \u201cLet\u2019s redesign the page,\u201d you commit to \u201cThis redesign should reduce hesitation at the pricing step, increasing trial starts without hurting lead quality.\u201d<\/p>\n\n\n\n

    In Conversion & Measurement<\/strong>, this matters because measurement is never neutral: which events you track, which segments you analyze, and which metric you call \u201cprimary\u201d all shape the outcome. A hypothesis makes those choices explicit, reducing the chance of cherry-picking metrics after the fact.<\/p>\n\n\n\n

    From a business value standpoint, hypotheses create competitive advantage by:<\/p>\n\n\n\n

      \n
    • increasing the percentage of experiments that generate meaningful learning,<\/li>\n
    • preventing wasted engineering and design cycles on ambiguous tests,<\/li>\n
    • and building a repeatable optimization system that scales beyond individual opinions.<\/li>\n<\/ul>\n\n\n\n

      In mature CRO<\/strong> programs, the hypothesis is also a communication tool\u2014aligning marketing, product, design, analytics, and leadership on what success means and what trade-offs are acceptable.<\/p>\n\n\n\n

      How Experiment Hypothesis Works<\/h2>\n\n\n\n

      In practice, an Experiment Hypothesis<\/strong> \u201cworks\u201d as a disciplined workflow that turns insight into a measurable decision.<\/p>\n\n\n\n

        \n
      1. \n

        Trigger (input): identify a problem or opportunity<\/strong>\n You start with evidence from Conversion & Measurement<\/strong>: funnel drop-offs, low add-to-cart rates, weak form completion, lower-than-expected activation, or qualitative feedback like session replays and surveys.<\/p>\n<\/li>\n

      2. \n

        Analysis (processing): diagnose why it\u2019s happening<\/strong>\n You look for friction sources, user intent mismatches, trust gaps, unclear value propositions, performance issues, or segmentation effects (new vs returning, mobile vs desktop, paid vs organic). The goal is to form a credible causal explanation.<\/p>\n<\/li>\n

      3. \n

        Execution (application): define the test and measurement plan<\/strong>\n You write the Experiment Hypothesis<\/strong>, specify the change, select a primary metric and guardrails, set the audience scope, and ensure tracking is correct. Then you run an experiment (often A\/B) or a controlled rollout.<\/p>\n<\/li>\n

      4. \n

        Outcome (output): decide and learn<\/strong>\n You evaluate results against the hypothesis, not just against a single uplift number. Even when the result is neutral, you document what you learned and how it updates future CRO<\/strong> priorities.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of Experiment Hypothesis<\/h2>\n\n\n\n

        A reliable Experiment Hypothesis<\/strong> usually contains the following elements:<\/p>\n\n\n\n

        1) Observation and insight<\/h3>\n\n\n\n

        What data suggests there\u2019s an issue or opportunity? This should come from Conversion & Measurement<\/strong> sources such as funnel analytics, cohort analysis, heatmaps, surveys, or support tickets.<\/p>\n\n\n\n

        2) Target audience and context<\/h3>\n\n\n\n

        Who is affected and where? Example: \u201cnew visitors on mobile landing pages from non-brand paid search.\u201d CRO outcomes can vary dramatically by segment, so this specificity prevents misleading averages.<\/p>\n\n\n\n

        3) Proposed change (the lever)<\/h3>\n\n\n\n

        What exactly will you change? A hypothesis should imply a clear manipulation: headline, layout, pricing display, trust elements, form fields, page speed improvements, or onboarding steps.<\/p>\n\n\n\n

        4) Causal reasoning (\u201cbecause\u201d)<\/h3>\n\n\n\n

        Why should the change work? The \u201cbecause\u201d is what turns a guess into an informed prediction. It might cite cognitive load, risk reduction, information scent, motivation\/ability, or relevance alignment.<\/p>\n\n\n\n

        5) Primary metric and decision rule<\/h3>\n\n\n\n

        Define success before you launch. In Conversion & Measurement<\/strong>, that means naming a primary KPI (for example, checkout completion rate) and guardrails (for example, average order value, refund rate, lead qualification rate).<\/p>\n\n\n\n

        6) Practical governance<\/h3>\n\n\n\n

        Who signs off, who implements, and who approves results? Strong CRO<\/strong> teams use lightweight governance: experiment tickets, documentation, QA checklists, and post-test readouts to prevent \u201csilent failures\u201d and tracking gaps.<\/p>\n\n\n\n

        Types of Experiment Hypothesis<\/h2>\n\n\n\n

        While there aren\u2019t rigid \u201cofficial\u201d types, there are practical categories that help teams structure their thinking:<\/p>\n\n\n\n

        Behavioral vs. technical hypotheses<\/h3>\n\n\n\n
          \n
        • Behavioral hypotheses<\/strong> predict a change in user decision-making (trust, clarity, motivation). <\/li>\n
        • Technical hypotheses<\/strong> predict a change due to performance or reliability (page speed, broken steps, latency).<\/li>\n<\/ul>\n\n\n\n

          Macro vs. micro conversion hypotheses<\/h3>\n\n\n\n
            \n
          • Macro conversion<\/strong> focuses on end goals (purchase, qualified lead, subscription). <\/li>\n
          • Micro conversion<\/strong> targets leading indicators (add-to-cart, CTA click, product view depth). In CRO<\/strong>, micro conversions are useful when macro events are too rare for fast learning, but they must correlate to business value.<\/li>\n<\/ul>\n\n\n\n

            Value proposition vs. friction reduction<\/h3>\n\n\n\n
              \n
            • Value proposition hypotheses<\/strong> increase perceived benefit (stronger messaging, proof, differentiation). <\/li>\n
            • Friction reduction hypotheses<\/strong> remove obstacles (shorter forms, clearer pricing, fewer steps).<\/li>\n<\/ul>\n\n\n\n

              Personalization\/segmentation hypotheses<\/h3>\n\n\n\n

              These predict that different audiences require different experiences. They are powerful but raise complexity in Conversion & Measurement<\/strong> (tracking, sample size, and interpretation).<\/p>\n\n\n\n

              Real-World Examples of Experiment Hypothesis<\/h2>\n\n\n\n

              Example 1: Ecommerce checkout trust and clarity<\/h3>\n\n\n\n

              Scenario:<\/strong> High drop-off at payment step on mobile.\nExperiment Hypothesis:<\/strong> If we add clear shipping\/returns details and trusted payment badges near the \u201cPay\u201d button for mobile users, then checkout completion rate will increase because it reduces perceived risk at the moment of purchase decision.\nMeasurement:<\/strong> Primary = checkout completion rate; Guardrails = refund rate, average order value.\nCRO tie-in:<\/strong> This is a friction-and-trust hypothesis grounded in observed funnel abandonment within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

              Example 2: SaaS trial activation and onboarding<\/h3>\n\n\n\n

              Scenario:<\/strong> Many trial signups, low activation (first key action).\nExperiment Hypothesis:<\/strong> If we replace a generic welcome screen with a role-based setup step that preconfigures the dashboard, then activation rate within 24 hours will increase because users reach \u201ctime to value\u201d faster.\nMeasurement:<\/strong> Primary = activation rate; Guardrails = support tickets per user, churn in first 14 days.\nCRO tie-in:<\/strong> This moves beyond acquisition into product-led CRO<\/strong>, using Conversion & Measurement<\/strong> across the full lifecycle.<\/p>\n\n\n\n

              Example 3: Lead generation form quality vs. volume<\/h3>\n\n\n\n

              Scenario:<\/strong> Marketing wants more leads; sales reports low quality.\nExperiment Hypothesis:<\/strong> If we add a single qualifying question and clarify \u201cwho this is for,\u201d then lead-to-opportunity rate will increase because expectations are set earlier, even if raw form submissions decrease.\nMeasurement:<\/strong> Primary = lead-to-opportunity rate; Secondary = form completion rate; Guardrails = cost per opportunity, time to first response.\nCRO tie-in:<\/strong> This reframes success around business outcomes, not vanity conversions\u2014an essential Conversion & Measurement<\/strong> discipline.<\/p>\n\n\n\n

              Benefits of Using Experiment Hypothesis<\/h2>\n\n\n\n

              A well-written Experiment Hypothesis<\/strong> delivers benefits that compound over time:<\/p>\n\n\n\n

                \n
              • Higher experiment quality:<\/strong> Clear causality and measurement reduce ambiguous tests.<\/li>\n
              • Faster learning cycles:<\/strong> Even \u201cfailed\u201d tests generate usable insight when the hypothesis is explicit.<\/li>\n
              • Better resource allocation:<\/strong> Engineering and design effort goes to tests tied to measurable outcomes.<\/li>\n
              • Reduced internal debate:<\/strong> Teams align on what \u201csuccess\u201d means, making decisions more objective.<\/li>\n
              • Improved customer experience:<\/strong> Hypotheses anchored in user friction and intent tend to improve clarity, trust, and usability\u2014core goals of CRO<\/strong> and Conversion & Measurement<\/strong> combined.<\/li>\n<\/ul>\n\n\n\n

                Challenges of Experiment Hypothesis<\/h2>\n\n\n\n

                An Experiment Hypothesis<\/strong> can still fail\u2014often for reasons that are fixable with better practice.<\/p>\n\n\n\n

                  \n
                • Weak causal reasoning:<\/strong> If \u201cbecause\u201d is vague, results are hard to interpret and replicate.<\/li>\n
                • Measurement limitations:<\/strong> If tracking is incomplete or attribution is noisy, Conversion & Measurement<\/strong> may not detect real impact.<\/li>\n
                • Sample size and duration constraints:<\/strong> Low traffic, high variability, or short run times can produce inconclusive outcomes.<\/li>\n
                • Confounding factors:<\/strong> Seasonality, campaigns, pricing changes, or site outages can contaminate results.<\/li>\n
                • Local maxima risk:<\/strong> Narrow CRO<\/strong> wins can harm brand perception or long-term value if guardrails aren\u2019t defined (for example, pushing urgency messaging that increases refunds).<\/li>\n<\/ul>\n\n\n\n

                  Best Practices for Experiment Hypothesis<\/h2>\n\n\n\n

                  Write hypotheses in a consistent, testable format<\/h3>\n\n\n\n

                  A practical template is:\nIf<\/strong> we change X for<\/strong> audience Y, then<\/strong> metric Z will change by direction D because<\/strong> reason R, as measured by<\/strong> M within timeframe T.<\/p>\n\n\n\n

                  Tie the hypothesis to a single primary metric<\/h3>\n\n\n\n

                  Pick one primary KPI to prevent post-hoc storytelling. Use guardrails to protect quality, revenue, or retention\u2014an essential habit in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                  Define \u201cwhy\u201d using evidence, not preference<\/h3>\n\n\n\n

                  Support the causal reasoning with funnel analysis, user research, or behavioral patterns. Strong CRO<\/strong> hypotheses are rarely based on aesthetics alone.<\/p>\n\n\n\n

                  Plan instrumentation and QA before launch<\/h3>\n\n\n\n

                  Confirm events, naming, segmentation, and edge cases. Many \u201cfailed\u201d experiments are actually tracking failures.<\/p>\n\n\n\n

                  Document learnings and update your playbook<\/h3>\n\n\n\n

                  Each Experiment Hypothesis<\/strong> should create reusable insight: which messages resonate, where friction lives, and which segments respond. Over time, this builds organizational memory and reduces repeated mistakes.<\/p>\n\n\n\n

                  Scale carefully<\/h3>\n\n\n\n

                  As experiment volume grows, manage interaction effects (multiple simultaneous tests) and standardize reporting so results remain trustworthy in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                  Tools Used for Experiment Hypothesis<\/h2>\n\n\n\n

                  An Experiment Hypothesis<\/strong> is not a tool, but it relies on systems that make testing and measurement reliable:<\/p>\n\n\n\n

                    \n
                  • Analytics tools:<\/strong> Funnel analysis, cohort tracking, path exploration, event debugging, segmentation, and retention views.<\/li>\n
                  • Experimentation platforms:<\/strong> A\/B testing, feature flagging, controlled rollouts, holdouts, and test scheduling to prevent overlapping conflicts.<\/li>\n
                  • Tag management and data layer systems:<\/strong> Consistent event definitions and easier QA for Conversion & Measurement<\/strong>.<\/li>\n
                  • Behavioral research tools:<\/strong> Heatmaps, scroll maps, session replays, on-page polls, and usability testing to inform the \u201cbecause.\u201d<\/li>\n
                  • CRM and marketing automation:<\/strong> Lead quality, pipeline impact, lifecycle stages, and downstream outcomes\u2014critical when CRO<\/strong> spans beyond the website.<\/li>\n
                  • Reporting dashboards:<\/strong> Standardized scorecards that show primary metric, guardrails, segments, and statistical confidence in one place.<\/li>\n<\/ul>\n\n\n\n

                    Metrics Related to Experiment Hypothesis<\/h2>\n\n\n\n

                    Metrics should match the promise of the Experiment Hypothesis<\/strong> and the business model:<\/p>\n\n\n\n

                      \n
                    • Conversion rate metrics:<\/strong> Purchase rate, trial start rate, lead form completion, checkout completion.<\/li>\n
                    • Revenue and value metrics:<\/strong> Revenue per visitor, average order value, customer lifetime value (modeled), lead-to-opportunity rate.<\/li>\n
                    • Efficiency metrics:<\/strong> Cost per acquisition, cost per qualified lead, time to activation, time to first value.<\/li>\n
                    • Engagement metrics:<\/strong> Click-through rate on key CTAs, scroll depth to critical content, onboarding step completion.<\/li>\n
                    • Quality guardrails:<\/strong> Refund rate, churn, spam rate, complaint rate, NPS\/CSAT (when available).<\/li>\n
                    • Experiment integrity metrics (often overlooked):<\/strong> Sample ratio mismatch checks, event firing rate, percent of \u201cunknown\u201d traffic, and latency\/performance impacts.<\/li>\n<\/ul>\n\n\n\n

                      In Conversion & Measurement<\/strong>, pairing a primary metric with guardrails is what keeps CRO<\/strong> honest and sustainable.<\/p>\n\n\n\n

                      Future Trends of Experiment Hypothesis<\/h2>\n\n\n\n

                      Several shifts are changing how Experiment Hypothesis<\/strong> is practiced within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                        \n
                      • More automation in insight generation:<\/strong> Teams increasingly use automated anomaly detection and pattern surfacing to identify where hypotheses should focus.<\/li>\n
                      • Privacy-driven measurement changes:<\/strong> With more restrictions on identifiers, hypotheses will lean more on first-party data, server-side event collection, and modeled conversions where appropriate.<\/li>\n
                      • Personalization at scale (with caution):<\/strong> Hypotheses will increasingly be segment-specific, but this raises complexity in sample sizes and interpretability\u2014pushing teams to be more rigorous in CRO<\/strong> governance.<\/li>\n
                      • Experimentation beyond the website:<\/strong> Product experiences, pricing pages, in-app onboarding, email journeys, and sales-assisted flows will be tested under the same hypothesis discipline.<\/li>\n
                      • Adaptive experimentation approaches:<\/strong> Methods like sequential testing and bandit-style allocation are gaining attention, but they require stronger statistical understanding and tighter Conversion & Measurement<\/strong> controls.<\/li>\n<\/ul>\n\n\n\n

                        Experiment Hypothesis vs Related Terms<\/h2>\n\n\n\n

                        Experiment Hypothesis vs. assumption<\/h3>\n\n\n\n

                        An assumption is an untested belief. An Experiment Hypothesis<\/strong> is an assumption made testable with a defined change and measurement plan. CRO maturity is often the shift from \u201cassumption-driven\u201d to \u201chypothesis-driven.\u201d<\/p>\n\n\n\n

                        Experiment Hypothesis vs. A\/B test<\/h3>\n\n\n\n

                        An A\/B test is a method. An Experiment Hypothesis<\/strong> is the reason you run the test and how you\u2019ll interpret outcomes. You can have an A\/B test with a weak hypothesis (low learning) or a strong hypothesis (high learning), even if both are statistically valid.<\/p>\n\n\n\n

                        Experiment Hypothesis vs. experiment design<\/h3>\n\n\n\n

                        Experiment design covers execution details: variants, targeting, randomization, duration, and statistical approach. The Experiment Hypothesis<\/strong> defines what you expect to happen and why\u2014guiding design choices and measurement priorities in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                        Who Should Learn Experiment Hypothesis<\/h2>\n\n\n\n
                          \n
                        • Marketers:<\/strong> To connect messaging and funnel changes to measurable business outcomes, not just engagement.<\/li>\n
                        • Analysts:<\/strong> To translate data findings into testable claims and reduce post-test ambiguity.<\/li>\n
                        • Agencies and consultants:<\/strong> To align stakeholders, document rationale, and deliver repeatable CRO<\/strong> improvements.<\/li>\n
                        • Business owners and founders:<\/strong> To make faster, safer decisions under uncertainty and prioritize experiments by potential impact.<\/li>\n
                        • Developers and product teams:<\/strong> To implement tests with clear success criteria, instrumentation needs, and guardrails\u2014strengthening Conversion & Measurement<\/strong> across the stack.<\/li>\n<\/ul>\n\n\n\n

                          Summary of Experiment Hypothesis<\/h2>\n\n\n\n

                          An Experiment Hypothesis<\/strong> is a clear, testable prediction about how a change will affect outcomes, grounded in evidence and paired with a measurement plan. It matters because it turns ideas into disciplined learning, strengthens decision-making, and improves repeatability within Conversion & Measurement<\/strong>. In CRO<\/strong>, it\u2019s the foundation that keeps experimentation focused on customer behavior and business impact\u2014not on opinions or isolated uplifts.<\/p>\n\n\n\n

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

                          1) What makes an Experiment Hypothesis \u201cgood\u201d?<\/h3>\n\n\n\n

                          A good Experiment Hypothesis<\/strong> is specific about the change, audience, expected direction of impact, and the metric that will prove or disprove it. It also explains the \u201cbecause\u201d in a way that\u2019s grounded in evidence.<\/p>\n\n\n\n

                          2) How detailed should an Experiment Hypothesis be?<\/h3>\n\n\n\n

                          Detailed enough that two different people would implement and measure the same test the same way. If it doesn\u2019t specify audience scope and primary metric, it\u2019s usually too vague for reliable Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                          3) Do I need a hypothesis for every CRO test?<\/h3>\n\n\n\n

                          Yes, if you want learning\u2014not just activity. Even small CRO<\/strong> tests benefit from a simple hypothesis because it prevents drifting into \u201cwe\u2019ll see what happens\u201d and improves post-test interpretation.<\/p>\n\n\n\n

                          4) What\u2019s the difference between a hypothesis and a KPI?<\/h3>\n\n\n\n

                          A KPI is what you track. An Experiment Hypothesis<\/strong> is a prediction about how a KPI will change due to a specific action, under defined conditions, within your Conversion & Measurement<\/strong> framework.<\/p>\n\n\n\n

                          5) What if the experiment result is inconclusive?<\/h3>\n\n\n\n

                          Treat it as a signal about measurement noise, sample size, or effect size. Re-check instrumentation, verify runtime and segmentation, and decide whether to iterate the hypothesis (change the lever or audience) or deprioritize the idea.<\/p>\n\n\n\n

                          6) How do I choose the primary metric for CRO experiments?<\/h3>\n\n\n\n

                          Pick the closest metric to the business outcome you\u2019re trying to influence, then add guardrails to protect long-term value. In Conversion & Measurement<\/strong>, this typically means one primary conversion metric plus 1\u20133 quality or revenue guardrails.<\/p>\n\n\n\n

                          7) Can an Experiment Hypothesis be used outside websites (email, product, ads)?<\/h3>\n\n\n\n

                          Yes. The same logic applies anywhere you can control exposure and measure outcomes: onboarding flows, lifecycle emails, pricing experiments, and even ad landing page-message alignment\u2014so long as your Conversion & Measurement<\/strong> setup can reliably attribute results.<\/p>\n","protected":false},"excerpt":{"rendered":"

                          An **Experiment Hypothesis** is the statement that turns an optimization idea into a testable claim\u2014one you can validate with data rather than opinion. In **Conversion & Measurement**, it acts as the bridge between what you observe (drop-offs, low engagement, weak leads) and what you change (copy, UX, offers, targeting) with clear expectations for impact and how you\u2019ll measure it.<\/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":[1889],"tags":[],"class_list":["post-7133","post","type-post","status-publish","format-standard","hentry","category-cro"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7133","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=7133"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7133\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7133"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7133"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7133"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}