{"id":7162,"date":"2026-03-24T02:32:03","date_gmt":"2026-03-24T02:32:03","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/minimum-detectable-effect\/"},"modified":"2026-03-24T02:32:03","modified_gmt":"2026-03-24T02:32:03","slug":"minimum-detectable-effect","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/minimum-detectable-effect\/","title":{"rendered":"Minimum Detectable Effect: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CRO"},"content":{"rendered":"\n

Minimum Detectable Effect is one of the most important (and most misunderstood) ideas in experimentation. In Conversion & Measurement<\/strong>, it answers a simple but high-stakes question: \u201cHow big of a change do we need to see before we can reliably detect it?\u201d<\/em> In CRO<\/strong>, that question determines whether an A\/B test is feasible, how long it should run, and whether \u201cno significant result\u201d actually means \u201cno impact\u201d or just \u201cnot enough data.\u201d<\/p>\n\n\n\n

Modern marketing teams run more experiments across websites, landing pages, onboarding flows, pricing pages, email, and paid traffic than ever before. Without a clear Minimum Detectable Effect, teams often underpower tests, misread outcomes, and waste traffic on experiments that were never capable of proving anything. Used well, Minimum Detectable Effect turns experimentation from guesswork into a disciplined Conversion & Measurement<\/strong> practice that scales.<\/p>\n\n\n\n

\n\n\n\n

2) What Is Minimum Detectable Effect?<\/h2>\n\n\n\n

Minimum Detectable Effect<\/strong> is the smallest true performance change (for a chosen metric) that your experiment is designed to reliably detect, given your assumptions about sample size, variability, confidence level, and statistical power.<\/p>\n\n\n\n

Beginner-friendly framing:<\/p>\n\n\n\n

    \n
  • If the Minimum Detectable Effect is +10% lift<\/strong>, your test is built to detect changes of about that size (or larger).<\/li>\n
  • If the true lift is only +2%<\/strong>, your test might not have enough data to confirm it\u2014even if the improvement is real.<\/li>\n<\/ul>\n\n\n\n

    The core concept is not \u201cWhat change do we hope for?\u201d but \u201cWhat change can we realistically measure with the traffic and time we have?\u201d<\/p>\n\n\n\n

    The business meaning is direct: Minimum Detectable Effect sets the line between detectable<\/strong> and indistinguishable from noise<\/strong>. In Conversion & Measurement<\/strong>, it connects experiment design to operational constraints (traffic volume, seasonality, budget, decision timelines). In CRO<\/strong>, it helps you prioritize tests that can create meaningful, measurable improvements instead of chasing tiny lifts you can\u2019t validate.<\/p>\n\n\n\n

    \n\n\n\n

    3) Why Minimum Detectable Effect Matters in Conversion & Measurement<\/h2>\n\n\n\n

    Minimum Detectable Effect matters because most experiment failures aren\u2019t caused by bad ideas\u2014they\u2019re caused by weak measurement design.<\/p>\n\n\n\n

    Key reasons it\u2019s strategically important in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

      \n
    • Prevents underpowered tests:<\/strong> If your Minimum Detectable Effect is too small for your traffic, you\u2019ll run tests that can\u2019t reach clarity.<\/li>\n
    • Improves decision quality:<\/strong> It reduces \u201cfalse negatives,\u201d where you conclude a change didn\u2019t work even though it did.<\/li>\n
    • Aligns experimentation with business impact:<\/strong> A test designed around a meaningful Minimum Detectable Effect encourages changes that move revenue, lead quality, retention, or costs\u2014not just vanity metrics.<\/li>\n
    • Creates competitive advantage:<\/strong> Teams that set realistic Minimum Detectable Effect thresholds run fewer, better tests and learn faster\u2014core to sustainable CRO<\/strong> velocity.<\/li>\n<\/ul>\n\n\n\n

      In short, Minimum Detectable Effect is a bridge between statistical rigor and real-world marketing operations.<\/p>\n\n\n\n

      \n\n\n\n

      4) How Minimum Detectable Effect Works<\/h2>\n\n\n\n

      Minimum Detectable Effect is conceptual, but it becomes practical through a repeatable planning flow:<\/p>\n\n\n\n

      1) Input \/ trigger: define the decision and metric<\/strong>\n Choose the primary metric (e.g., conversion rate, signup completion, revenue per visitor) and the decision you want to make (ship, iterate, or stop).<\/p>\n\n\n\n

      2) Analysis: model detectability given constraints<\/strong>\n You estimate baseline performance, expected variability, and the sample size you can realistically collect. You also choose a confidence level (significance threshold) and statistical power. These choices determine the Minimum Detectable Effect your test can support.<\/p>\n\n\n\n

      3) Execution: design the experiment around that threshold<\/strong>\n You set test duration, traffic allocation, and guardrails (e.g., don\u2019t ship if refunds increase). In CRO<\/strong>, this is where Minimum Detectable Effect influences prioritization: bigger changes for low-traffic pages; more subtle optimizations for high-traffic pages.<\/p>\n\n\n\n

      4) Output \/ outcome: interpret results through the lens of detectability<\/strong>\n If the test is inconclusive, you ask: \u201cWas the Minimum Detectable Effect too small? Did we collect enough sample? Did variance increase?\u201d In Conversion & Measurement<\/strong>, this keeps you from over-interpreting noisy results.<\/p>\n\n\n\n

      \n\n\n\n

      5) Key Components of Minimum Detectable Effect<\/h2>\n\n\n\n

      A useful Minimum Detectable Effect calculation depends on a handful of core elements:<\/p>\n\n\n\n

        \n
      • Primary metric definition:<\/strong> Precise event definitions, attribution windows, and data inclusion rules (e.g., exclude internal traffic, bots, duplicates).<\/li>\n
      • Baseline rate and variability:<\/strong> The starting conversion rate (or mean value) and how much it naturally fluctuates.<\/li>\n
      • Sample size and traffic availability:<\/strong> Visitors, sessions, users, emails delivered, or eligible conversions\u2014based on what you\u2019re testing.<\/li>\n
      • Significance level and power assumptions:<\/strong> These govern how cautious you are about false positives and false negatives. They meaningfully change Minimum Detectable Effect.<\/li>\n
      • Test design choices:<\/strong> A\/B vs multivariate, equal splits vs weighted, sequential vs fixed horizon, and segmentation strategy.<\/li>\n
      • Governance and responsibilities:<\/strong> Who approves the Minimum Detectable Effect target, who validates data quality, and who owns \u201cgo\/no-go\u201d decisions in CRO<\/strong> programs.<\/li>\n<\/ul>\n\n\n\n

        In mature Conversion & Measurement<\/strong> teams, Minimum Detectable Effect is documented in the experiment brief before a test launches.<\/p>\n\n\n\n

        \n\n\n\n

        6) Types of Minimum Detectable Effect<\/h2>\n\n\n\n

        Minimum Detectable Effect doesn\u2019t have \u201cformal types\u201d in the way ad formats do, but there are practical distinctions that matter:<\/p>\n\n\n\n

        Absolute vs relative Minimum Detectable Effect<\/h3>\n\n\n\n
          \n
        • Absolute change:<\/strong> \u201cIncrease conversion rate from 3.0% to 3.3%\u201d (a +0.3 percentage point shift).<\/li>\n
        • Relative change:<\/strong> \u201cIncrease conversion rate by 10%\u201d (from 3.0% to 3.3%).<\/li>\n<\/ul>\n\n\n\n

          Both are valid; relative is often easier for stakeholders, while absolute is sometimes clearer for modeling and forecasting.<\/p>\n\n\n\n

          Metric-based Minimum Detectable Effect<\/h3>\n\n\n\n
            \n
          • Rate metrics:<\/strong> conversion rate, click-through rate, activation rate.<\/li>\n
          • Average\/continuous metrics:<\/strong> revenue per visitor, average order value, time to activation.<\/li>\n
          • Count-based metrics:<\/strong> number of qualified leads, trials started (often still modeled as rates once normalized).<\/li>\n<\/ul>\n\n\n\n

            Practical vs statistical Minimum Detectable Effect<\/h3>\n\n\n\n

            A test can be able to detect a very small effect statistically, yet that effect may be too small to matter financially. Strong CRO<\/strong> teams set Minimum Detectable Effect based on business materiality<\/em>, not just statistical possibility.<\/p>\n\n\n\n

            \n\n\n\n

            7) Real-World Examples of Minimum Detectable Effect<\/h2>\n\n\n\n

            Example 1: E-commerce checkout simplification<\/h3>\n\n\n\n

            A retailer wants to remove a field from checkout to reduce friction. Baseline purchase rate is stable, but traffic is modest.<\/p>\n\n\n\n

              \n
            • The team sets a Minimum Detectable Effect that reflects a meaningful business win (e.g., a lift that would clearly justify engineering work).<\/li>\n
            • In Conversion & Measurement<\/strong>, they verify that payment failures and refund rates are guardrails.<\/li>\n
            • In CRO<\/strong>, they prioritize a bigger UX change (likely larger impact) rather than a subtle button color tweak that would require far more traffic to detect.<\/li>\n<\/ul>\n\n\n\n

              Example 2: SaaS pricing page experiment<\/h3>\n\n\n\n

              A SaaS company tests monthly vs annual emphasis on the pricing page.<\/p>\n\n\n\n

                \n
              • The primary metric is trial-to-paid conversion or revenue per visitor, not just clicks.<\/li>\n
              • Because revenue metrics have higher variability, the Minimum Detectable Effect will often be larger than for a simple click metric.<\/li>\n
              • The team uses the Minimum Detectable Effect to set expectations: \u201cWe can detect meaningful revenue shifts; smaller changes may be inconclusive without longer duration.\u201d<\/li>\n<\/ul>\n\n\n\n

                Example 3: Lead gen landing page with segmented traffic<\/h3>\n\n\n\n

                An agency runs a landing page test across brand vs non-brand traffic.<\/p>\n\n\n\n

                  \n
                • They avoid splitting into too many segments initially, because segmentation reduces sample size per group and inflates the Minimum Detectable Effect.<\/li>\n
                • Instead, they design the test to detect an overall lift first, then follow up with segment-focused analysis if the effect is large enough.<\/li>\n
                • This approach keeps Conversion & Measurement<\/strong> credible and improves CRO<\/strong> throughput.<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n

                  8) Benefits of Using Minimum Detectable Effect<\/h2>\n\n\n\n

                  Using Minimum Detectable Effect intentionally delivers practical advantages:<\/p>\n\n\n\n

                    \n
                  • Higher learning velocity:<\/strong> Fewer \u201cwasted\u201d tests that cannot reach clarity.<\/li>\n
                  • Better prioritization:<\/strong> Focus on hypotheses capable of producing detectable impact with available traffic.<\/li>\n
                  • Cost savings:<\/strong> Reduced time spent building and analyzing low-signal experiments; smarter use of paid traffic and development resources.<\/li>\n
                  • Cleaner stakeholder communication:<\/strong> Clear expectations about what your experiment can and cannot prove.<\/li>\n
                  • Improved customer experience:<\/strong> More emphasis on meaningful changes (speed, clarity, trust) rather than superficial tweaks\u2014often the heart of effective CRO<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n

                    9) Challenges of Minimum Detectable Effect<\/h2>\n\n\n\n

                    Minimum Detectable Effect is powerful, but it has real pitfalls:<\/p>\n\n\n\n

                      \n
                    • Unreliable baselines:<\/strong> Seasonality, campaigns, tracking changes, or site outages can make baseline rates unstable, corrupting Minimum Detectable Effect assumptions.<\/li>\n
                    • Metric noise and variance:<\/strong> Revenue per visitor and downstream metrics are valuable but noisy; detectability gets harder.<\/li>\n
                    • Too many segments:<\/strong> Over-segmentation increases the Minimum Detectable Effect and creates inconclusive results.<\/li>\n
                    • Misaligned incentives:<\/strong> Teams sometimes choose an unrealistically small Minimum Detectable Effect to justify running a test, then fail to reach enough sample size.<\/li>\n
                    • Data quality limitations:<\/strong> Attribution gaps, cookie loss, consent effects, and cross-device behavior complicate Conversion & Measurement<\/strong> and can blur detectability.<\/li>\n<\/ul>\n\n\n\n

                      In CRO<\/strong>, these challenges often show up as \u201cWe ran the test for weeks and learned nothing.\u201d<\/p>\n\n\n\n

                      \n\n\n\n

                      10) Best Practices for Minimum Detectable Effect<\/h2>\n\n\n\n

                      Actionable ways to use Minimum Detectable Effect well:<\/p>\n\n\n\n

                        \n
                      • Start with business materiality:<\/strong> Define the smallest change worth shipping (financially and operationally), then see if it\u2019s detectable with your traffic.<\/li>\n
                      • Choose one primary metric per test:<\/strong> Secondary metrics are useful, but multiple \u201cprimary\u201d outcomes create confusion and inflate false discovery risk.<\/li>\n
                      • Stabilize the baseline before testing:<\/strong> Avoid launching during major promo periods unless the experiment is designed around them.<\/li>\n
                      • Use guardrails:<\/strong> Track quality metrics (refunds, churn, support contacts, bounce rate) so you don\u2019t \u201cwin\u201d the primary metric while harming the business.<\/li>\n
                      • Avoid premature peeking:<\/strong> Re-checking results too frequently can increase false positives unless you use methods designed for it.<\/li>\n
                      • Document assumptions:<\/strong> Write down baseline, Minimum Detectable Effect target, expected runtime, and stop conditions. This improves Conversion & Measurement<\/strong> governance and CRO<\/strong> consistency.<\/li>\n
                      • Iterate with bigger swings on low traffic:<\/strong> If a page gets limited volume, focus on larger, higher-leverage changes to reduce the Minimum Detectable Effect burden.<\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n

                        11) Tools Used for Minimum Detectable Effect<\/h2>\n\n\n\n

                        Minimum Detectable Effect isn\u2019t a \u201ctool feature\u201d as much as a capability created by your stack and process. Common tool categories in Conversion & Measurement<\/strong> and CRO<\/strong> include:<\/p>\n\n\n\n

                          \n
                        • Analytics tools:<\/strong> Validate baseline rates, segment traffic, monitor anomalies, and confirm event definitions.<\/li>\n
                        • Experimentation platforms:<\/strong> Randomize exposure, manage variants, enforce audience rules, and report outcomes with statistical methods.<\/li>\n
                        • Tag management systems:<\/strong> Control tracking changes, reduce implementation errors, and standardize event naming.<\/li>\n
                        • Data warehouses and BI dashboards:<\/strong> Reconcile experiment data with revenue systems, subscription status, refunds, and lifecycle metrics.<\/li>\n
                        • CRM systems and marketing automation:<\/strong> Connect experiments to lead quality, pipeline outcomes, and retention signals.<\/li>\n
                        • Reporting and governance workflows:<\/strong> Experiment briefs, QA checklists, and review cadences to ensure Minimum Detectable Effect assumptions remain valid.<\/li>\n<\/ul>\n\n\n\n

                          The most important \u201ctool\u201d is a disciplined experiment design process that keeps Conversion & Measurement<\/strong> consistent across teams.<\/p>\n\n\n\n

                          \n\n\n\n

                          12) Metrics Related to Minimum Detectable Effect<\/h2>\n\n\n\n

                          Minimum Detectable Effect connects to metrics that influence detectability and decision-making:<\/p>\n\n\n\n

                            \n
                          • Baseline conversion rate (or baseline mean):<\/strong> Determines the starting point and impacts variance modeling.<\/li>\n
                          • Sample size per variant:<\/strong> The single biggest lever affecting detectability.<\/li>\n
                          • Standard deviation \/ variance:<\/strong> Especially important for revenue and time-based metrics.<\/li>\n
                          • Confidence level (significance threshold):<\/strong> A stricter threshold typically increases the Minimum Detectable Effect.<\/li>\n
                          • Statistical power:<\/strong> Higher desired power usually increases required sample size to detect the same Minimum Detectable Effect.<\/li>\n
                          • Effect size (observed lift):<\/strong> What the test reports; compare it to your Minimum Detectable Effect target.<\/li>\n
                          • Guardrail metrics:<\/strong> Churn, refund rate, complaint rate, bounce rate, engagement quality.<\/li>\n<\/ul>\n\n\n\n

                            Strong CRO<\/strong> programs treat these as a system: you don\u2019t \u201coptimize conversion\u201d in isolation.<\/p>\n\n\n\n

                            \n\n\n\n

                            13) Future Trends of Minimum Detectable Effect<\/h2>\n\n\n\n

                            Several trends are reshaping how Minimum Detectable Effect is applied in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                              \n
                            • AI-assisted experimentation:<\/strong> Automation can suggest hypotheses, predict likely effect ranges, and flag when observed variance makes the Minimum Detectable Effect unrealistic.<\/li>\n
                            • More adaptive testing approaches:<\/strong> Teams increasingly adopt sequential methods and adaptive allocation to reduce wasted traffic while maintaining rigor.<\/li>\n
                            • Personalization and smaller segments:<\/strong> Personalization creates many micro-audiences, which increases Minimum Detectable Effect challenges due to reduced sample sizes. Expect more emphasis on pooling strategies and hierarchical modeling.<\/li>\n
                            • Privacy and measurement constraints:<\/strong> Consent requirements and identity loss can reduce observable sample size and increase noise, raising the Minimum Detectable Effect for many tests.<\/li>\n
                            • Incrementality discipline:<\/strong> As marketing pushes toward incrementality, Minimum Detectable Effect will be used more often to design experiments that prove true causal lift, not just correlated movement.<\/li>\n<\/ul>\n\n\n\n

                              As CRO<\/strong> expands beyond web pages into product-led growth and lifecycle optimization, Minimum Detectable Effect becomes even more central.<\/p>\n\n\n\n

                              \n\n\n\n

                              14) Minimum Detectable Effect vs Related Terms<\/h2>\n\n\n\n

                              Minimum Detectable Effect vs statistical significance<\/h3>\n\n\n\n
                                \n
                              • Statistical significance<\/strong> describes whether an observed result is unlikely under a \u201cno difference\u201d assumption.<\/li>\n
                              • Minimum Detectable Effect<\/strong> describes what magnitude of difference your test is built to reliably detect.\nYou can have a non-significant result because the true effect is smaller than your Minimum Detectable Effect\u2014not because there is no effect.<\/li>\n<\/ul>\n\n\n\n

                                Minimum Detectable Effect vs statistical power<\/h3>\n\n\n\n
                                  \n
                                • Power<\/strong> is the probability your test will detect a true effect of a certain size.<\/li>\n
                                • Minimum Detectable Effect<\/strong> is the effect size you pair with a power target to plan sample size.\nThey are two sides of experiment planning in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                  Minimum Detectable Effect vs effect size (observed lift)<\/h3>\n\n\n\n
                                    \n
                                  • Effect size<\/strong> is what happened in the data.<\/li>\n
                                  • Minimum Detectable Effect<\/strong> is what you designed the test to be able to detect.\nIn CRO<\/strong>, comparing observed lift to Minimum Detectable Effect prevents overconfidence in tiny wins.<\/li>\n<\/ul>\n\n\n\n
                                    \n\n\n\n

                                    15) Who Should Learn Minimum Detectable Effect<\/h2>\n\n\n\n

                                    Minimum Detectable Effect is useful across roles:<\/p>\n\n\n\n

                                      \n
                                    • Marketers:<\/strong> Set realistic expectations for campaign and landing page tests; avoid chasing unmeasurable micro-lifts.<\/li>\n
                                    • Analysts:<\/strong> Design better experiments, interpret null results correctly, and improve Conversion & Measurement<\/strong> credibility.<\/li>\n
                                    • Agencies:<\/strong> Scope experimentation roadmaps based on client traffic and business impact; defend recommendations with rigor.<\/li>\n
                                    • Business owners and founders:<\/strong> Make faster, higher-confidence decisions about product and funnel changes tied to revenue.<\/li>\n
                                    • Developers:<\/strong> Implement experiments and tracking with clearer requirements, reducing rework and measurement disputes.<\/li>\n<\/ul>\n\n\n\n

                                      If you run tests, read test results, or fund testing\u2014Minimum Detectable Effect belongs in your CRO<\/strong> toolbox.<\/p>\n\n\n\n

                                      \n\n\n\n

                                      16) Summary of Minimum Detectable Effect<\/h2>\n\n\n\n

                                      Minimum Detectable Effect<\/strong> is the smallest change your experiment is designed to reliably detect, given traffic, variance, and statistical assumptions. It matters because it prevents underpowered testing, improves prioritization, and turns inconclusive experiments into actionable learning. In Conversion & Measurement<\/strong>, it connects analytics realities to decision-making. In CRO<\/strong>, it guides which experiments are worth running, how long to run them, and how to interpret \u201cno significant difference\u201d responsibly.<\/p>\n\n\n\n

                                      \n\n\n\n

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

                                      1) What is Minimum Detectable Effect in plain language?<\/h3>\n\n\n\n

                                      Minimum Detectable Effect is the smallest improvement (or decline) your test can reliably pick up with the data you expect to collect. If the real change is smaller than that threshold, your results may look like noise.<\/p>\n\n\n\n

                                      2) How do I choose a good Minimum Detectable Effect for my business?<\/h3>\n\n\n\n

                                      Pick the smallest change that would be worth implementing if true (time, engineering cost, risk), then check whether your traffic can detect it within a reasonable duration. If not, raise the Minimum Detectable Effect by testing bigger changes or consolidate traffic.<\/p>\n\n\n\n

                                      3) What happens if my Minimum Detectable Effect is too small?<\/h3>\n\n\n\n

                                      Your required sample size becomes very large, so the test may run too long, be disrupted by seasonality, or never reach clarity. In Conversion & Measurement<\/strong>, this often leads to inconclusive outcomes and stakeholder frustration.<\/p>\n\n\n\n

                                      4) Can I use Minimum Detectable Effect for metrics beyond conversion rate?<\/h3>\n\n\n\n

                                      Yes. Minimum Detectable Effect applies to revenue per visitor, average order value, retention, activation, and other outcomes. Just note that noisier metrics often require larger sample sizes to detect the same relative change.<\/p>\n\n\n\n

                                      5) How does Minimum Detectable Effect affect CRO prioritization?<\/h3>\n\n\n\n

                                      In CRO<\/strong>, it helps you favor tests with potential impact large enough to be detectable. Low-traffic pages usually need bigger changes; high-traffic pages can validate smaller optimizations faster.<\/p>\n\n\n\n

                                      6) If a test is not significant, does that mean the change didn\u2019t work?<\/h3>\n\n\n\n

                                      Not necessarily. It may mean the true effect is smaller than your Minimum Detectable Effect, or that variance\/baseline instability reduced detectability. Review sample size, data quality, and whether your assumptions still held.<\/p>\n\n\n\n

                                      7) Does segmentation change the Minimum Detectable Effect?<\/h3>\n\n\n\n

                                      Yes. Segmenting reduces the sample size per group, which typically increases the Minimum Detectable Effect. Segment only when you have enough volume or when segmentation is essential to the decision you\u2019re making.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                      Minimum Detectable Effect is one of the most important (and most misunderstood) ideas in experimentation. In **Conversion & Measurement**, it answers a simple but high-stakes question: *\u201cHow big of a change do we need to see before we can reliably detect it?\u201d* In **CRO**, that question determines whether an A\/B test is feasible, how long it should run, and whether \u201cno significant result\u201d actually means \u201cno impact\u201d or just \u201cnot enough data.\u201d<\/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-7162","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\/7162","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=7162"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7162\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7162"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7162"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7162"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}