Audit Sampling Methods

“25 samples” is not best practice anymore. Better sampling approaches are. In SOX testing, I still hear: “It’s a daily control — pick 25 samples.” But here’s the truth 👇 25 is not magic. It’s a fallback. That number comes from legacy IIA guidance and Big 4 non-statistical sampling tables — meant for a time when testing more wasn’t practical. Today, better options exist. The real question isn’t 25 vs 40. It’s how much assurance are we really getting? Here’s what’s actually better than fixed 25-sample testing 👇 1. Risk-Based Sampling (better than fixed 25) Instead of: “This is a daily control → pick 25” You do: -Identify high-risk periods (quarter ends, year end, spike months) -Focus on judgmental samples, not purely random -Sample fewer items, but risk-relevant items 👉 Example Instead of 25 random JEs: -Pick 12–15 JEs -All from quarter-end, manual postings, unusual users 📌 Better assurance than 25 random samples 2. Stratified Sampling (Big 4 preferred) Population is split into risk buckets, then sampled. Example for payments: -High-value payments → test 100% -Medium-value → sample a few -Low-value → minimal or none 👉 Result: Total samples may be less than 25 But coverage of material risk is higher 📌 This is explicitly supported by Big 4 and IIA guidance. 3. Data Analytics / 100% Population Testing (BEST) This is the gold standard and the real answer to your question. Instead of sampling: -Run analytics on 100% of the population -Identify exceptions -Then do targeted follow-up testing Examples: 100% JE testing for approvals, posting time, users 100% payment testing for duplicate, override, threshold breaches 📌 When you test 100%, the question of “25 vs 40” disappears. Sampling exists only because we can’t test everything and Analytics removes that limitation. 4. Fully Automated Controls (No sampling) If a control is: Fully automated and No manual intervention is required If Strong ITGCs in place 👉 You don’t need 25 samples. 👉 You test design + configuration. This is explicitly supported by: IIA ,PCAOB and Big 4 SOX methodologies So what is “BEST” instead of 25? 🔥 Best-practice hierarchy 1. 100% population testing via analytics 2. Risk-based / stratified sampling 3. Judgmental sampling focused on high-risk periods 4. Fixed 25 samples (only when above aren’t feasible) A strong line you can confidently use (review-proof) “We didn’t select 25 samples. We applied risk-based sampling supported by analytics to obtain higher assurance than traditional sampling.” That line works with: -Audit committees -External auditors -Big 4 reviewers -PCAOB logic So yes — 25 samples is acceptable. But it’s rarely optimal.The future of SOX isn’t bigger samples. It’s smarter evidence. #SOX #InternalAudit #AuditSampling #IIA #Big4 #RiskManagement #ControlsTesting #AuditAnalytics #Governance

Mastering SOX Sampling: When it comes to SOX compliance, sampling isn't just about numbers; it's about strategy. The goal? Ensuring internal controls over financial reporting are effective without wasting resources. By tailoring your sampling approach to risks and control types, you can turn a complex task into a streamlined process. Cracking the Code of Sampling Sampling for SOX audits isn’t one-size-fits-all. It depends on factors like control nature, frequency, risk level, and population size. High-risk areas and frequent controls demand more attention, while lower-risk processes allow you to scale back. Frequency Shapes Sampling 1. Annual Controls: Rarely occurring controls, like annual reconciliations, need only 1–2 samples. Think of these as low-maintenance checkpoints. 2. Quarterly Controls: Testing 2–4 samples is sufficient to ensure at least half the year's activity is covered. Ideal for quarterly reviews or board presentations. 3. Monthly Controls: Sample selection ranges from 2–10. Low-risk processes? Stick to 2–3 months. High-risk areas like cash flow? Bump it up to 7–10 months for confidence. 4. Daily/Weekly Controls: These require rigorous testing—25–40 samples. Why? Because frequent processes, like user access reviews or revenue tracking, carry higher variability. For stable processes, 25 may suffice. For high-stakes controls, go up to 40. The Sampling Toolbox 1. Random Sampling:The classic approach for fairness and objectivity. 2. Systematic Sampling:Perfect for orderly datasets—pick every "nth" item. 3. Judgmental Sampling:Ideal for targeting high-risk or unusual cases. 4. Stratified Sampling:Divides the population into groups to focus where it matters most. Sampling for Control Types Approvals & Authorizations: Manual controls? Test 25–40 samples based on risk. Automated controls? Test one instance per period and validate configurations. Reconciliations: For monthly reconciliations, test 2–3 months of activity. Change Management: Evaluate 10–25 changes to ensure proper authorization and documentation. ITGCs: Randomly select samples from user accounts, configurations, or system changes to ensure IT controls are effective. Dealing with Exceptions What happens when a sample fails? For high-risk controls, it’s a red flag—expand testing or reassess risks. For low-risk controls, document findings and evaluate if compensating controls mitigate the issue. Pro Tips for Sampling Success 1. Leverage Technology: Use tools like ACL, IDEA, or Excel to automate sampling and analysis. 2. Adapt to Risks: Be flexible—adjust sample sizes as risks evolve. 3. Document Everything: From methods to findings, ensure every detail is recorded. Sampling for SOX compliance doesn’t have to be overwhelming. By focusing on risks,control frequency, and population size,you can ensure reliable results and a smoother audit process. When done right, sampling isn’t just a compliance exercise—it’s a way to add real value to your organisation.

How do Internal Auditors select samples? Is it random or planned? The answer is simple — it depends on the risk and objective of the audit. In most cases, auditors use a mix of methods to select samples: • Risk-Based Sampling: Focus more on areas where the risk of error or fraud is higher. • Random Sampling: To give all transactions an equal chance of being selected, ensuring fairness. • Judgmental Sampling: Based on auditor’s experience, past issues, or observations during process walkthroughs. • Stratified Sampling: Dividing data into groups like high-value, medium, and low, and selecting samples from each. The idea is not to check everything but to check the right things, so that key risks are covered, and meaningful insights are provided to management. In simple terms, smart sample selection helps auditors focus their time and effort where it matters most.

Internal Audit Sampling is the process auditors use to select and test a portion of transactions, balances, or data rather than reviewing all available information. It helps auditors form conclusions about the entire population efficiently and effectively. Here’s a clear breakdown 👇 🔹 1. Purpose of Sampling in Internal Audit To evaluate controls and substantive procedures without testing every transaction. To save time and resources while maintaining a reasonable level of assurance. To identify trends, errors, or control weaknesses that may exist in the broader population. 🔹 2. Types of Sampling A. Statistical Sampling Uses probability theory to select samples, allowing for measurable confidence levels. Random sampling: Every item has an equal chance of being selected. Systematic sampling: Every nth item is selected. Stratified sampling: The population is divided into groups (strata), and samples are taken from each group. B. Non-Statistical (Judgmental) Sampling Based on the auditor’s professional judgment. Haphazard sampling: Items selected without structured technique. Block sampling: A contiguous block (e.g., one month’s transactions). Judgmental sampling: Auditor focuses on high-risk, large, or unusual items. 🔹 3. Steps in Audit Sampling Define audit objective (e.g., test approval of purchase orders). Identify population (e.g., all purchase orders in Q2 2025). Determine sampling method (statistical or judgmental). Decide sample size (based on risk, control effectiveness, materiality). Select sample items. Perform testing on the selected sample. Evaluate results — extrapolate findings to the population and determine if controls are effective or errors are material. 🔹 4. Factors Influencing Sample Size Audit risk (higher risk → larger sample). Expected error rate. Tolerable error. Population size. Nature of control (manual vs. automated). 🔹 5. Example If an auditor wants to test whether all purchase orders above $10,000 are approved by the procurement manager, they might: Define population: all POs > $10,000 during Q2. Choose systematic sampling: every 10th PO. Test selected POs for approval signatures. If 2 out of 25 lack approval, assess control deficiency and estimate potential error rate in the full population. 🔹 6. Documentation Internal auditors should document: Sampling objective and population. Sampling technique and rationale. Sample size determination. Results of testing and evaluation of errors.

Internal Audit Month: Day 18 Audit Sampling Techniques Audit sampling is the process of selecting a representative subset of data to draw conclusions about an entire population. Since auditors rarely examine 100% of transactions, sampling helps balance efficiency with assurance. There are two main approaches: 1. Statistical sampling (e.g., random selection, systematic sampling) – allows measurable confidence levels 2. Non-statistical sampling (e.g., judgmental selection) – relies on auditor experience and risk assessment Common Techniques: - Random sampling: Every item has an equal chance of selection - Systematic sampling: Selecting every nth item (e.g., every 10th invoice) - Stratified sampling: Dividing data into groups (e.g., high-value vs low-value transactions) and sampling each group - Judgmental sampling: Selecting items based on risk (e.g., unusual or large transactions) Practical Example: An auditor is reviewing 500 supplier invoices. Instead of checking all: They may stratify the population—review all invoices above ₦5 million (high risk), Then apply random sampling to the remaining invoices. This ensures focus on material and risky items, while still maintaining coverage across the population. Why it matters in the workplace: Good sampling improves audit quality by ensuring conclusions are reliable, defensible, and efficient. Poor sampling, on the other hand, can lead to wrong conclusions and missed risks. In simple terms, audit sampling helps you work smarter—without compromising assurance.

📑Audit Sampling and Testing: Key Insights Audit sampling is a fundamental aspect of audit procedures, allowing auditors to evaluate financial statements effectively without testing entire populations. The content covers essential aspects of audit testing and sampling, including: ✅Types of Audit Tests 1️⃣Tests of Controls – Assess the effectiveness of internal controls through inquiries, inspections, observations, walkthroughs, and re-performance. 2️⃣Substantive Tests – Detect material misstatements in financial statements through: Substantive Tests of Transactions Substantive Tests of Account Balances Analytical Procedures ✅Audit Sampling Audit sampling applies audit procedures to a subset of transactions or balances to draw conclusions about the entire population. ✅Types of Sampling Risk: 1️⃣Sampling Risk – The risk that a selected sample may not represent the population. 2️⃣Non-Sampling Risk – The risk of errors due to improper audit procedures or misinterpretation of results. ✅Types of Audit Sampling: 1️⃣Non-Statistical Sampling 2️⃣Statistical Sampling ✅Audit Sampling Process 1️⃣Define Objectives – Determine whether controls are effective or if account balances are misstated. 2️⃣Determine Sample Size – Based on acceptable risk, tolerable deviation, expected misstatement, and population characteristics. 3️⃣Select Sample Items – Using random, systematic, stratified, or block sampling methods. 4️⃣Perform Audit Procedures – Apply substantive or control tests to the selected sample. 5️⃣Evaluate Results – Calculate deviation rates, analyze misstatements, and determine audit conclusions. 6️⃣Draw Conclusions – Compare the projected misstatement to tolerable limits and decide on further actions. 7️⃣Document Procedures – Record all sampling steps and justifications for conclusions. Effective audit sampling ensures efficiency, enhances risk assessment, and strengthens audit conclusions while maintaining compliance with professional auditing standards. #Audit #Sampling

Sampling in SOX Audits – The Auditor’s Balancing Act In SOX (Sarbanes-Oxley) audits, we often face a key challenge: How do we test control effectiveness without checking every single transaction? That’s where sampling comes in. Sampling helps us test a representative subset of transactions or controls to draw reliable conclusions for the whole population, saving both time and cost while maintaining audit quality. Key Steps in SOX Sampling: 1️⃣ Define the Population – Ensure completeness & accuracy (C&A). 2️⃣ Choose the Right Sampling Method – Random, judgmental, or systematic. 3️⃣ Determine Sample Size – Based on control frequency, risk rating & audit guidance. 4️⃣ Select the Sample – Use audit tools (e.g., ACL, AuditBoard, Excel macros). 5️⃣ Test & Document – Perform control testing and record deviations. Why It Matters in SOX: ✅ Meets PCAOB/AICPA standards ✅ Provides reasonable assurance without 100% testing ✅ Helps auditors focus on high-risk areas ✅ Ensures efficient use of audit resources Tip: Always ensure the sample selection process is transparent and reproducible. Documentation is key for external reviewers. #SOX #ITAudit #Sampling #Compliance #InternalControls #AuditBestPractices

7 Different Types of Statistical Sampling and their Use Cases in Data Science 🧬 Sampling is a fundamental concept in statistics and data science used to draw conclusions about a population by examining a subset of it. Here’s a breakdown of different types of sampling methods and their use cases: 1. Simple Random Sampling Description: Each member of the population has an equal chance of being selected. This can be done using random number generators or drawing lots. Use Cases: • Surveys: Ensuring that every individual in a survey has an equal chance of being selected. • Quality Control: Randomly selecting products from a batch for testing to ensure quality. 2. Systematic Sampling Description: Members of the population are selected at regular intervals. For example, every nth member is chosen. Use Cases: • Manufacturing: Sampling every 10th item in a production line to check quality. • Polling: Selecting every 5th person on a list to participate in a survey. 3. Stratified Sampling Description: The population is divided into distinct subgroups (strata) based on a characteristic (e.g., age, income), and a random sample is taken from each subgroup. Use Cases: • Market Research: Ensuring that different demographic groups are represented proportionally in surveys. • Medical Trials: Ensuring that different age groups or health conditions are adequately represented. 4. Cluster Sampling Description: The population is divided into clusters (e.g., geographic areas), and a random sample of clusters is selected. All members within chosen clusters are then surveyed. Use Cases: • Epidemiological Studies: Selecting specific regions or cities to study health patterns. • Educational Research: Sampling schools or classrooms rather than individual students. 5. Convenience Sampling Description: Samples are taken from a group that is easy to access or convenient. This method is often used when time or resources are limited. Use Cases: • Initial Research: Pilot studies or preliminary research where resources are constrained. • Public Opinion Polls: Using readily available participants like social media followers. 6. Judgmental Sampling (Purposive Sampling) Description: The researcher selects the sample based on their judgment and specific criteria. It’s often used when specific characteristics or expertise are needed. Use Cases: • Expert Opinions: Consulting a select group of experts for in-depth insights. • Case Studies: Focusing on particular instances that are believed to be informative. 7. Snowball Sampling Description: Used for populations that are hard to access. Initial participants are selected and then asked to refer others, creating a “snowball” effect. Use Cases: • Social Network Studies: Researching hard-to-reach populations like marginalized communities or rare diseases. • Qualitative Research: Exploring relationships and networks within a specific group.

I have been exploring the topic of audit sampling, particularly from a random sampling perspective to understand how many samples are typically sufficient, especially when dealing with large populations running into thousands. I was primarily interested in identifying an effective random sampling strategy that could help minimize detection risk. This question also stems from discussions with first-line teams striving to remain audit ready. It’s important for them to have a well-articulated rationale for how they gain comfort over the effectiveness of their processes and controls, and how they demonstrate that readiness from an audit perspective. While I was able to find some of the answers I was looking for, captured in the attached slides. I also included some fundamental concepts that may be widely known, but I felt were essential for this consolidation. The analysis is based on my own judgment, shaped by past experiences, and is supported by methodology references from AICPA, The Institute of Internal Auditors Inc. (IIA), and COSO (Committee of Sponsoring Organizations of the Treadway Commission) frameworks, with a focus on ensuring that control testing produces reliable, unbiased, and representative results when random sampling is used. I would love your thoughts, please have a look and let me know if there’s anything you’d like to add or discuss further. Anup Singh, CISA® P.S. The presentation is fairly basic in terms of visuals. My focus was more on the content rather than design, so apologies if the graphics aren't particularly eye-catching. #InternalAudit #ControlTesting #RiskManagement #AuditSampling #IIA #COSO #AICPA #Governance #AuditTechniques #DetectionRisk #AuditInsights #Compliance #DataDrivenAuditing #LinkedIn LinkedIn LinkedIn for Learning LinkedIn Guide to Creating

Simple Random Sampling vs. Stratified Sampling! In statistics, selecting the right sampling method is pivotal, especially when dealing with varied population characteristics that could influence your results. Probabilistic techniques like simple random sampling and stratified sampling both produce unbiased estimates of the population mean, yet they differ significantly in their impact on data variation. Therefore, choosing wisely between them can dramatically enhance your data analysis outcomes. 🟢 For example, the benefit of stratification is clearly shown in the simulation below. Stratified sampling produces a tighter distribution of sample means around the population mean, compared to simple random sampling. This method not only maintains the unbiased nature of your estimates but also narrows confidence intervals, enabling more powerful statistical testing! 🟢 Namely, both methods produce an unbiased estimate of the population mean (41.2), but the key difference lies in the variation. Stratified sampling significantly reduces the variation, thereby increasing the power of the statistical testing. 🟢 So, recognizing distinct characteristics in the population (such as minority and majority groups in our case) and addressing them in sampling reduces the overall variation! This concept extends to machine learning as well, particularly in how data is handled during model training. Similar to how stratified sampling can improve statistical tests, stratified k-fold cross-validation ensures that each fold reflects the overall class distribution, which is crucial for training robust models in cases of class imbalance. When your data exhibits significant variability or class imbalance, opting for stratified techniques over simple random sampling can lead to more reliable and insightful outcomes. PS: When using stratified sampling, it is crucial to preserve the population structure. For instance, if your population consists of 20% from Class A and 80% from Class B, your sample should reflect these proportions accurately. In fact, this is the advantage of stratification over simple random sampling. #Statistics #DataScience #MachineLearning #SamplingMethods #DataAnalysis #StratifiedSampling #StatisticalTesting #Imbalancedata