Marketing Analytics, Attribution, and Experimentation

Introduction

Digital marketing creates an unusual managerial advantage: many interactions can be recorded at relatively low cost. A startup can observe impressions, clicks, visits, sign-ups, purchases, product usage, referrals, and cancellations. Yet abundant data does not automatically produce good decisions. Tracking may be incomplete, metrics may be defined inconsistently, attribution may confuse association with causation, and experiments may be underpowered or poorly implemented.

Marketing analytics is therefore not simply the production of dashboards. It is a disciplined process of translating strategic questions into measurable constructs, collecting valid data, analyzing customer behavior, making decisions under uncertainty, and learning through controlled or quasi-controlled comparisons. For startups, this discipline is particularly important because resources are scarce and early conclusions can determine which channels, audiences, and business models receive investment.

This chapter develops an integrated approach to measurement planning, funnel and cohort analysis, retention, attribution, incrementality, experimentation, and managerial communication. It also considers privacy, governance, artificial intelligence, and the limits of digital evidence.

Learning Objectives

By the end of this chapter, readers should be able to:

1. Construct a measurement plan linked to business objectives and decision rights.
2. Design metric definitions, event taxonomies, dashboards, and data-quality controls.
3. Analyze acquisition funnels, cohorts, retention, customer behavior, and unit economics.
4. Compare attribution models and explain why attribution is not the same as incrementality.
5. Design and interpret A/B and multivariate experiments, including power and validity concerns.
6. Communicate analytical findings with appropriate attention to uncertainty, bias, privacy, and causal limitations.

1. From Business Questions to Measurement Systems

A measurement system should begin with a decision, not with the data that happen to be available. A useful sequence is:

Strategic objective → decision question → customer behavior → metric → data source → analysis → action.

For example, a startup seeking profitable growth may ask: “Should we increase paid social spending?” The relevant behaviors might include qualified visits, activation, purchase, repeat purchase, and contribution margin. Counting impressions alone cannot answer the question.

1.1 Objectives, metrics, and indicators

An objective describes a desired organizational outcome, such as profitable customer growth. A key performance indicator (KPI) is a selected measure used to monitor progress toward that outcome. A diagnostic metric helps explain why a KPI changed. A guardrail metric identifies harmful side effects.

A metric must have a precise definition. “Conversion rate,” for instance, may mean purchasers divided by sessions, unique visitors, leads, or exposed users. These denominators are not interchangeable.

1.2 North-star metrics and metric hierarchies

A north-star metric is a high-level measure intended to represent delivered customer value and strategic progress. It should not be selected merely because it is large, easy to increase, or favorable to reporting. A useful north-star metric is supported by a hierarchy:

• Outcome level: retained customers or contribution profit.
• Value level: successful uses, completed projects, or recurring orders.
• Behavior level: activation, invitations, or feature adoption.
• Input level: qualified traffic, content production, or response time.

The hierarchy prevents teams from optimizing an input metric while damaging the business outcome. For example, a campaign can increase clicks through sensational creative while reducing qualified leads.

2. Measurement Plans and Data Architecture

A measurement plan is a formal specification of what will be measured, why it matters, how it will be collected, and who owns the resulting decision. At minimum, it should include:

2.1 Event taxonomy

An event taxonomy names and structures the behavioral events recorded by a product or marketing system. A robust event should include an event name, timestamp, anonymous or authorized user identifier, session identifier where appropriate, source information, device or platform, and relevant properties. For example, trial_started might include plan type, acquisition source, and experiment variant.

Events should be defined according to stable business behavior rather than changing interface labels. “Clicked_blue_button” is fragile; “checkout_started” is more analytically meaningful. Names should be documented in a data dictionary, version-controlled, and tested in development and production environments.

2.2 Data quality and identity resolution

Common quality failures include duplicate events, missing campaign parameters, inconsistent time zones, bot traffic, ad-blocking, cookie deletion, cross-device fragmentation, and changes in tracking implementation. Identity resolution attempts to connect events belonging to the same person or account. Deterministic methods, such as an authenticated account identifier, are generally more defensible than probabilistic matching, which can create false links.

A dashboard should not conceal uncertainty caused by incomplete observation. Teams should distinguish observed users from estimated users and annotate major instrumentation changes. A sudden increase in conversion may represent genuine improvement—or a broken denominator.

3. Core Quantitative Measures

3.1 Rates and averages

A rate is generally a count of events or people satisfying a condition divided by an appropriate population. For a simple conversion rate:

The numerator must be included in the denominator’s population and time period. If 240 of 6,000 eligible visitors purchase, the conversion rate is 240 ÷ 6,000 = 0.04 = 4%.

Average order value (AOV) is:

Averages can conceal skew. Median order value, percentiles, and distributions should be examined when a small number of customers generate unusually large purchases.

3.2 Customer acquisition cost and contribution economics

Customer acquisition cost (CAC) is commonly defined as acquisition-related expenditure divided by newly acquired customers during a specified period:

The definition must specify whether it includes media only or also creative, agency, sales, and onboarding costs. A simplified contribution margin is:

Variable costs may include payment fees, fulfillment, customer support attributable to usage, and cost of goods sold. Profitability comparisons should use consistent time windows and cohorts.

A frequently used simplified customer lifetime value estimate for a subscription business is:

ARPU is average revenue per user per period, gross margin percentage is expressed as a decimal, and churn rate is the proportion of active customers leaving per period. This steady-state approximation assumes relatively stable revenue, margin, and churn, and it ignores discounting, expansion revenue, acquisition timing, and finite customer lives. It should not be treated as a precise forecast when these assumptions fail.

3.3 Funnel analysis

A funnel describes sequential stages such as impression, click, landing-page visit, sign-up, activation, purchase, and repeat purchase. Stage conversion from stage i to stage i+1 is:

Overall conversion across a strictly sequential funnel is the product of stage conversions:

This multiplication applies when the stages are defined consistently and the population flows sequentially. If users can skip stages, return to earlier stages, or be counted differently, a simple funnel may misrepresent behavior. A path analysis or state-transition model may be more suitable.

Worked Example: Funnel diagnosis

Suppose 10,000 qualified visitors produce 1,000 sign-ups, 400 activated accounts, and 80 paid customers. Visitor-to-sign-up conversion is 10%; sign-up-to-activation conversion is 40%; activation-to-paid conversion is 20%. Overall visitor-to-paid conversion is 80 ÷ 10,000 = 0.8%, which also equals 0.10 × 0.40 × 0.20. Improving activation from 40% to 50%, while holding other stages constant, produces an overall rate of 1.0% and 100 paid customers from the same 10,000 visitors. The example identifies activation as a potentially valuable lever, but it does not prove that changing activation will cause the predicted improvement; an experiment is needed.

4. Cohorts, Retention, and Customer Behavior

A cohort is a group sharing a defined starting event or characteristic, commonly the week or month of acquisition. Cohort analysis prevents the mixing of new and mature customers. It is especially useful when acquisition volume grows, because aggregate retention can appear stable even while newer cohorts deteriorate.

Retention can be defined in several ways:

• Classic retention: the proportion of a cohort active at a specified later time.
• Rolling retention: whether a customer is active at or after a specified time.
• Unbounded retention: whether a customer returns at any point after acquisition.
• Revenue retention: retained revenue rather than retained customer count.

A retention rate for cohort c at time t can be written as:

The definition of “active” must represent meaningful value, not merely an open or accidental visit. For a collaboration product, an active user might complete a project action; for commerce, it might be a completed order.

A cohort table can show acquisition month in rows and months since acquisition in columns. It should report cohort size, retention, revenue, refunds, and possibly contribution margin. Small cohorts produce volatile percentages; counts and confidence intervals should accompany rates where practical.

Customer behavior beyond the funnel

Useful behavioral analyses include frequency, recency, monetary value, feature adoption, search behavior, content engagement, referral activity, and cancellation reasons. Segmentation should be theoretically or operationally meaningful. Arbitrary micro-segments can encourage overfitting and generate contradictory recommendations.

Survival analysis is useful when the outcome is time until churn or purchase and some observations have not yet experienced the event. Censoring occurs when observation ends before the event is observed. Treating every censored customer as a retained customer can bias estimates.

5. Dashboards and Communication

A dashboard is an instrument for recurring decisions, not a warehouse of every available metric. Effective dashboards have a clear audience, refresh schedule, owner, definitions, comparison baseline, and action rule. A founder dashboard may emphasize cash, contribution profit, payback, retention, and channel efficiency; a growth team dashboard may add experiment status, funnel transitions, and creative diagnostics.

A strong analytical narrative usually follows:

1. Observation: What changed, for which population, and over what period?
2. Interpretation: What explanations are consistent with the evidence?
3. Decision: What should be changed, continued, or investigated?
4. Uncertainty: What data limitations or alternative explanations remain?

Analysts should distinguish descriptive statements (“conversion was higher”) from causal statements (“the redesign increased conversion”). The second requires a credible counterfactual.

6. Attribution and Incrementality

6.1 What attribution does

Attribution assigns credit for an observed outcome to marketing touchpoints. Common rules include:

Attribution is useful for organizing observed journeys and diagnosing tracking coverage. It does not by itself show what would have happened without a touchpoint. A user who clicks a retargeting advertisement immediately before purchasing may already have intended to buy.

6.2 Incrementality and counterfactual reasoning

Incrementality concerns the causal effect of an intervention. For a treatment group and a comparable control group, an intuitive difference-in-means estimate is:

The control group approximates the counterfactual outcome that treated units would have experienced without treatment. Valid interpretation requires appropriate assignment or credible assumptions. Randomization supports comparability in expectation; observational comparisons require stronger assumptions, such as no unmeasured confounding and adequate overlap.

Incremental return on ad spend can be expressed as:

This differs from reported revenue divided by attributed spend. Incremental profit should use the same population, time horizon, attribution window, and cost basis on both sides.

6.3 Methods for estimating incrementality

• Randomized holdout: eligible users, regions, or accounts are randomly withheld from treatment.
• Geo experiments: geographic units are assigned to treatment or control; interference and unequal market composition must be considered.
• Ghost ads: eligible users are randomized, but control users are not shown the ad; exposure logs help preserve eligibility information.
• Difference-in-differences: compares treatment-control changes over time, relying substantially on a parallel-trends assumption.
• Matched observational analysis: constructs comparable groups but remains vulnerable to unobserved differences.

Interference occurs when one person’s treatment affects another person’s outcome, as with social sharing or household purchasing. Spillovers can contaminate control groups and reduce estimated effects.

7. Experimentation as a Learning System

An experiment should begin with a falsifiable hypothesis. “The shorter checkout will improve completed purchases without increasing refunds” is preferable to “We will test a better checkout.” The primary outcome, eligible population, treatment definition, randomization unit, duration, stopping rule, and guardrails should be specified before launch.

7.1 A/B testing

In an A/B test, eligible units are randomly assigned to control or treatment. Randomization may occur at the user, account, session, region, or other unit, but the unit must match the mechanism of interference and the level at which outcomes are measured.

For binary outcomes, the treatment conversion estimate is:

and the control estimate is:

where x is the number of conversions and n is the number of eligible randomized units. The absolute effect is p_T − p_C; the relative lift, when p_C > 0, is:

For inference about a difference in independent proportions, a large-sample standard error under the null hypothesis can use the pooled proportion:

where p̂ = (x_T + x_C) ÷ (n_T + n_C). This approximation requires sufficiently large expected counts and independent observations. Exact or alternative methods may be preferable for small samples, clustered assignment, or rare outcomes.

A confidence interval communicates estimation uncertainty. For a rough large-sample interval for a difference in proportions, one may use:

where z is the selected standard-normal critical value and SE is an appropriate standard error. The interval should not be interpreted as a guarantee that the true effect lies inside it; it describes uncertainty under the model and sampling assumptions.

7.2 Power, sample size, and stopping

Power is the probability of detecting a specified effect if that effect truly exists, at a chosen significance threshold. Sample-size planning depends on baseline rate, minimum detectable effect, significance level, desired power, allocation ratio, variance, and dependence structure. Stopping repeatedly when a result becomes favorable inflates false-positive risk unless sequential methods or pre-specified rules are used.

A statistically detectable effect may be commercially trivial. Conversely, a practically important effect may fail to reach conventional significance because the sample is small. Decisions should consider effect size, interval width, cost, risk, and strategic relevance—not only a p-value.

7.3 Multiple testing and heterogeneity

Testing many outcomes, variants, or segments increases the chance of at least one apparently positive result by chance. Pre-specify a primary outcome, treat secondary outcomes as exploratory unless adjusted, and replicate important findings. Segment effects should be interpreted cautiously because random variation and multiple comparisons can create persuasive but unstable patterns.

Heterogeneous treatment effects are real possibilities: a feature may help new users but confuse experienced users. Prespecified interaction analyses are more credible than searching retrospectively through dozens of segments.

7.4 Multivariate experiments

A multivariate test changes multiple elements simultaneously and estimates combinations of factors. It can be efficient when traffic is abundant, but it requires sufficient observations for each cell and careful interpretation of interactions. If factor A and factor B interact, the effect of A depends on the level of B. Sparse cells, implementation errors, and too many combinations make multivariate testing risky for early-stage startups.

8. Illustrative Case: Vela, a Subscription Productivity Startup

Background

Vela offers a collaborative planning application. Its paid-social dashboard reported strong click-through rates and a favorable last-touch ROAS, but cash generation remained weak. The growth team proposed increasing retargeting expenditure.

Problem

An audit found that campaign parameters were missing for some mobile traffic, “activation” was defined differently in product and marketing systems, and retargeting received most last-touch credit. The team also observed that newer acquisition cohorts had lower 30-day retention.

Analysis

The team created a measurement plan with a single activation definition: a new account had to create a project, invite a collaborator, and complete one planning action within seven days. It rebuilt cohort tables by acquisition week and separated paid, organic, referral, and direct traffic. The analysis showed:

• Retargeting clicks were concentrated among users who had already visited several times.
• Last-touch reporting overstated retargeting’s apparent role.
• Paid-social cohorts had adequate trial starts but weak activation.
• Mobile users experienced a slower invitation flow.

The team did not conclude that retargeting had zero value. Instead, it identified a causal question: what is the incremental effect of retargeting among eligible users?

Approach and solution

Vela ran a randomized user-level holdout among eligible retargeting audiences for four weeks. The primary outcome was paid subscription within 30 days; guardrails were complaint rate and unsubscribe rate. In parallel, it ran an A/B test of the mobile invitation flow, randomizing new accounts after sign-up. The primary product outcome was seven-day activation, with 30-day paid conversion as a secondary outcome.

The tests were registered internally before launch, and the team avoided reallocating traffic based on early daily fluctuations. Results were analyzed by intention to treat: users were evaluated according to their assigned condition, whether or not they actually saw an ad or completed the redesigned flow.

Outcome

The holdout showed that retargeting generated fewer incremental subscriptions than last-touch reporting implied. It remained useful for a narrow high-intent segment, but broad spending was reduced. The invitation redesign increased activation, while the confidence interval for paid conversion remained wide. Vela therefore adopted the redesign, continued measurement, and avoided claiming a definitive revenue effect.

Learning points

1. A dashboard can be internally consistent yet strategically misleading.
2. Attribution is a descriptive allocation rule; holdouts address incrementality.
3. Product friction may be a larger growth constraint than traffic volume.
4. Guardrails prevent optimization of one metric at the expense of customer experience.
5. Uncertainty should shape the next decision rather than be hidden in reporting.

9. Ethics, Privacy, and Governance

Marketing analytics involves behavioral data that may be personal, sensitive, or linkable to individuals. Responsible practice requires a lawful basis for processing, purpose limitation, data minimization, retention controls, access controls, security, and transparent notices. Consent mechanisms should not be manipulative, and withdrawing consent should be practical where applicable.

Teams should avoid collecting data merely because it might become useful. Sensitive attributes should not be used to target or exclude people in ways that create unlawful or unjust discrimination. Lookalike modeling and automated bidding can reproduce historical inequities even when protected characteristics are not directly included, because correlated variables may act as proxies.

Governance should include a metric dictionary, experiment registry, data lineage, role-based permissions, audit logs, deletion procedures, and review of high-risk uses. Analysts should document whether a result is descriptive, predictive, or causal. These categories imply different levels of evidentiary support.

10. Artificial Intelligence and Contemporary Developments

AI can assist with anomaly detection, audience clustering, creative generation, forecasting, and natural-language summaries. It can also introduce fabricated explanations, leakage from future data, unstable segments, automation bias, and privacy risks. AI-generated insight should be traceable to source data, reproducible where possible, and reviewed by a person with domain responsibility.

Privacy restrictions and platform changes are increasing the importance of first-party data, modeled conversions, clean rooms, server-side measurement, and media-mix or geo-based analysis. Modeled data can be useful, but estimates should be labeled as estimates and periodically validated against known outcomes. No technical architecture removes the need for causal design and governance.

Challenges and Limitations

Marketing analytics is constrained by nonrandom exposure, missing data, changing platforms, seasonality, delayed conversions, attribution-window choices, identity fragmentation, bot activity, survivorship bias, and organizational incentives. A/B tests can fail because of contamination, novelty effects, low power, implementation errors, or outcomes measured too early. Even a valid experiment may have limited generalizability: an effect observed among current users in one market may not apply to a new audience or future product version.

The most defensible practice is triangulation. Combine clean descriptive measurement, cohort analysis, experiments, qualitative research, financial analysis, and domain knowledge. When causal certainty is unavailable, communicate the strength of evidence and make reversible decisions where possible.

Best-Practice Checklist

• Link every important metric to a decision.
• Define numerator, denominator, population, time window, and grain.
• Maintain an event dictionary and test instrumentation.
• Use cohorts rather than relying only on aggregate performance.
• Separate attributed outcomes from incremental outcomes.
• Pre-specify experiment hypotheses, primary outcomes, guardrails, and stopping rules.
• Report effect sizes and uncertainty, not only significance labels.
• Investigate data quality before interpreting large changes.
• Protect privacy and restrict access to necessary data.
• Record assumptions, limitations, and decisions in an experiment registry.
• Prefer durable learning over short-term metric improvement.

Chapter Summary

Marketing analytics is a decision discipline that connects objectives, customer behavior, metrics, data, analysis, and action. Measurement plans establish definitions, ownership, data sources, and quality rules. Funnels reveal stage-specific losses, while cohorts and retention analysis show how customer value changes over time. CAC, contribution margin, and carefully qualified LTV estimates connect marketing activity to economics.

Attribution allocates observed credit but does not establish causality. Incrementality requires a counterfactual, most credibly created through randomization or supported by explicit quasi-experimental assumptions. A/B tests can estimate causal effects when assignment, measurement, sample size, and stopping practices are appropriate. Multivariate tests offer efficiency but demand substantial traffic and careful analysis of interactions.

Finally, dashboards and analytical narratives should communicate decisions, effect sizes, uncertainty, and limitations. Privacy, fairness, governance, and responsible use of AI are central requirements rather than peripheral concerns. The mature startup treats analytics as an iterative learning system, not as a mechanism for producing impressive numbers.

Key Terms

Attribution: A rule for assigning observed outcomes to marketing touchpoints.

Cohort: A group sharing a defined starting event or characteristic.

Conversion rate: The proportion of an eligible population completing a specified outcome.

Counterfactual: The outcome that would have occurred for the same unit under an alternative condition.

Customer acquisition cost (CAC): Acquisition expenditure divided by newly acquired customers under a stated definition.

Dashboard: A recurring visual or tabular display organized around decisions and performance monitoring.

Event taxonomy: A documented system for naming and structuring behavioral events.

Experiment: A planned comparison designed to estimate the effect of an intervention.

Funnel: A sequence of customer stages used to analyze progression and loss.

Guardrail metric: A measure used to detect harmful effects while optimizing a primary outcome.

Incrementality: The causal change attributable to a marketing intervention.

Intention-to-treat: Analysis based on assigned condition regardless of actual treatment exposure.

Lifetime value (LTV): The economic value expected from a customer over a defined relationship period.

Metric grain: The observational level of a record, such as event, session, user, order, or account.

North-star metric: A high-level measure intended to represent delivered customer value and strategic progress.

Power: The probability of detecting a specified effect under stated assumptions.

Retention: The proportion of a cohort remaining active according to a defined activity rule.

Statistical significance: Evidence assessed under a specified statistical model and null hypothesis; it is not equivalent to managerial importance.

Treatment effect: The difference in outcomes caused by assignment to an intervention relative to a counterfactual condition.

Validity: The degree to which a measurement or inference accurately supports its intended interpretation.

Review Questions

Short Answer Questions

1. Why should a measurement plan begin with a decision question rather than with available data?
2. Distinguish a KPI, a diagnostic metric, and a guardrail metric.
3. What is the difference between attribution and incrementality?
4. Why are acquisition cohorts useful when a startup is growing rapidly?
5. State two reasons why repeated stopping in an A/B test can be misleading.

Long Answer Questions

1. Design a measurement plan for a two-sided marketplace. Include objectives, funnel events, primary KPIs, guardrails, data sources, and data-quality rules.
2. Compare last-touch, linear, time-decay, and data-driven attribution. Explain when each may be useful and why none necessarily estimates causal impact.
3. Explain how retention analysis, contribution economics, CAC, and LTV can be combined to evaluate a startup’s growth strategy.
4. Describe the assumptions and threats to validity in a randomized marketing holdout and a difference-in-differences design.
5. Develop a complete A/B testing protocol for a checkout redesign, including hypothesis, randomization unit, outcomes, sample-size considerations, stopping rule, and interpretation.

Analytical / Application Questions

1. A startup has 12,000 eligible visitors, 1,200 sign-ups, 480 activated accounts, and 96 paid customers. Calculate each sequential conversion rate and the overall visitor-to-paid conversion rate. Identify one reason the results might be invalid if users can skip stages.
2. A retargeting campaign receives most last-touch credit, but a randomized holdout shows only a small difference in paid conversion. Explain how both findings can be true and recommend a budget decision process.
3. An experiment increases activation by 8% relative but has a wide confidence interval for revenue and a small increase in complaint rate. Recommend whether to roll out, continue, modify, or stop the test, and justify the decision using effect size, uncertainty, and guardrails.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes attribution from incrementality?

A. Attribution measures only organic traffic, whereas incrementality measures only paid traffic.

B. Attribution allocates observed credit, whereas incrementality estimates the causal change caused by an intervention.

C. Attribution requires randomization, whereas incrementality never uses a control group.

D. Attribution is always more accurate than experimentation.

Correct Answer: B. Attribution allocates observed credit, whereas incrementality estimates the causal change caused by an intervention.

Question 2

A startup records 300 purchases from 7,500 eligible visitors. What is the visitor-to-purchase conversion rate?

A. 2%

B. 4%

C. 7.5%

D. 25%

Correct Answer: B. 4%

Question 3

Which metric is most appropriately classified as a guardrail for a checkout experiment?

A. Checkout completion rate

B. Number of experiment impressions

C. Refund rate

D. Treatment sample size

Correct Answer: C. Refund rate

Question 4

What is the principal purpose of cohort analysis?

A. To assign all conversions to the final marketing touchpoint

B. To combine new and mature customers into one aggregate rate

C. To compare groups sharing a defined starting point over time

D. To eliminate the need for event tracking

Correct Answer: C. To compare groups sharing a defined starting point over time

Question 5

Which condition is most directly supported by random assignment in an A/B test?

A. Guaranteed business profitability

B. Comparable treatment and control groups in expectation

C. Zero measurement error

D. Identical customer responses across all segments

Correct Answer: B. Comparable treatment and control groups in expectation

Question 6

If 80 of 2,000 treatment users convert and 60 of 2,000 control users convert, what is the absolute difference in conversion rates?

A. 0.5 percentage points

B. 1 percentage point

C. 2 percentage points

D. 7 percentage points

Correct Answer: B. 1 percentage point

Question 7

Why can last-touch attribution overstate the value of retargeting?

A. Retargeting is never shown to existing visitors.

B. The final touch may reach users who already had strong purchase intent.

C. Last-touch models give all credit to the first interaction.

D. Retargeting cannot be measured digitally.

Correct Answer: B. The final touch may reach users who already had strong purchase intent.

Question 8

Which practice most directly reduces false positives from repeatedly checking experiment results?

A. Changing the primary outcome after launch

B. Using a pre-specified stopping rule or an appropriate sequential method

C. Increasing the number of dashboard colors

D. Reporting only the most favorable segment

Correct Answer: B. Using a pre-specified stopping rule or an appropriate sequential method

Question 9

A simplified subscription LTV approximation uses ARPU, gross margin percentage, and periodic churn. Which assumption is most important to its steady-state interpretation?

A. Revenue and churn are reasonably stable over the modeled period.

B. Every customer has identical behavior and tenure.

C. All marketing channels have equal attribution.

D. The product has no variable costs.

Correct Answer: A. Revenue and churn are reasonably stable over the modeled period.

Question 10

Which statement about a statistically significant result is correct?

A. It necessarily represents a large and profitable effect.

B. It proves the result will generalize to every future population.

C. It provides evidence against a specified null model but must still be assessed for practical importance and validity.

D. It eliminates the need to inspect data quality.

Correct Answer: C. It provides evidence against a specified null model but must still be assessed for practical importance and validity.