Drafting Hypotheses

🎯 Why Most Business Problems Remain Unsolved (And How to Fix That) Last week, I had the privilege of facilitating a Problem Solving & Business Acumen workshop for our teams at L'Oréal Indonesia. 💡 The Problem We All Face (But Rarely Talk About) Here's an uncomfortable truth: we're wired to jump to solutions. In business, this looks like: ✔️ Launching promotions without understanding why sales declined ✔️ Hiring more people without diagnosing process inefficiencies ✔️ Copying competitor tactics without validating if they fit our context The cost? Wasted resources, frustrated teams, and recurring problems that never truly go away. According to the World Economic Forum's Future of Jobs Report 2023, analytical and critical thinking are the #1 and #2 most important skills for workers. Yet, most of us were never formally taught how to think critically or solve problems systematically. 🛠️ The Problem-Solving Process: A Step-by-Step Guide Step 1: Define the Problem (Don't Jump to Judgment!) 📝 Craft a Problem Statement with 6 components: "How can [responsible party] improve/reduce [reality] to meet [expectation] within [timeline] without [anti-goals], in order to fulfill [reason]?" Example: "How can the product team launch a new product on time in Q4 2024 without sacrificing key processes, in order to meet the sales target?" Step 2: Find Alternatives (Issue Tree + MECE) Once the problem is clear, break it down using an Issue Tree. For instance, if mascara sales dropped -14% YoY: 📦 Placement → Gondola compliance, visibility, signage 🎁 Promotion → BOGO mechanics, POS materials 💰 Price → Elasticity, perceived value 🎨 Product Claims → Content freshness, reviews 🔥 Competition → Share of voice, endcap presence ✅ Ensure hypotheses are MECE (Mutually Exclusive, Collectively Exhaustive)—no overlaps, no gaps. Step 3: Test Your Hypotheses Don't fall in love with your first idea. Run quick tests: 📊 For a skincare serum declining in pharmacies, we tested: ✔️ Hypothesis A: Reduced pharmacist advocacy is the issue → Micro-detailing pilot in 10 stores ✔️ Hypothesis B: Cold chain OOS drives lost sales → Warehouse SOP audit + temperature logs ✔️ Hypothesis C: Execution gaps suppress promo ROI → Endcap compliance audit Each hypothesis had clear KPIs and timelines—no guessing, just data. Step 4: Make the Decision (Impact vs. Effort Matrix) Not all solutions are equal. Prioritize: 🟩 Quick wins—do this! 🟦 Strategic bets 🟨 Fill-ins 🟥 Avoid Focus on low effort, high impact moves first. Build momentum, then tackle the big bets. 🚨 What Happens When We Skip These Steps? A mascara brand saw sales drop -14% YoY. The reaction? "Let's run a BOGO promo!" The result? Sales stayed flat. Why? Because the real issues were: ❌ Poor gondola compliance (only 68% correct facings) ❌ Weak influencer share of voice ❌ Competitor secured prime endcap space The lesson: Solutions applied to the wrong problem = wasted budget and missed targets.

When a business grows rapidly, the cracks in your processes start to show. That’s exactly what happened to us As our team scaled, it became clear: not everyone understood the hypothesis-generation process in the same way. This caused confusion, inconsistent problem-solving, and slowed down decision-making So, we developed a clear format to align everyone, newcomers and veterans alike, around structured, high-impact hypotheses. It starts with identifying the bottleneck In ecommerce, this might mean noticing that users drop off before completing a purchase The first instinct? "Add trust badges at checkout" But that’s too vague Is the real issue trust? A confusing checkout? Delivery costs? We learned to dig deeper: Problem: Low checkout conversion because users lack trust Action: Add trust badges (e.g., privacy policy, money-back guarantees) Expected result: Increase conversion from 20% to 40% 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 + 𝗔𝗰𝘁𝗶𝗼𝗻 + 𝗘𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝗥𝗲𝘀𝘂𝗹𝘁 This structure keeps our hypotheses focused and testable We prioritize using the ICE framework (Impact, Confidence, Ease). Doesn’t matter if we sum or multiply the values; the important part is consistent prioritization Then, we hold regular meetings: 1) Prepare hypotheses with a defined problem and goal 2) Refine and discuss existing ideas 3) Only brainstorm new ones when we’ve addressed the current list The result? A ready-to-implement hypothesis that’s documented from start to finish. This documentation becomes gold when reviewing what worked and what didn’t Fast growth demands clarity. Rebuilding internal processes isn’t just helpful, it’s necessary What’s your go-to method for hypothesis generation?

One thing I’ve learned as a PM is how to craft the right hypotheses. Every product decision begins with a bet—a hypothesis about what will work, why it will work, and for whom. Yet, many teams rush past this critical step, treating it as a formality rather than the foundation of success. Here’s why getting your hypothesis right can make or break your product: 1️⃣ Hypotheses Are Decision-Making Anchors Your hypothesis is your compass. It clarifies: - What you believe: e.g., “Users will pay more for faster delivery.” - Why you believe it: e.g., “Survey data shows 65% of users value speed over cost.” - How to measure success: e.g., “A 20% uptick in conversion rate during checkout.” Without this clarity, you risk pursuing features based on gut instinct or surface-level trends, leading to misallocated resources and product churn. 2️⃣ Wrong Hypothesis? Wrong Product. A wrong hypothesis leads to a wrong product faster than you can pivot. Take time to validate your assumptions before building. The earlier you test your hypothesis, the cheaper failure becomes. 3️⃣ Crafting Strong Hypotheses Is a Skill A good hypothesis is: - Specific: It clearly defines the “who,” “what,” and “why.” - Testable: You can prove or disprove it through data. - Impactful: If true, it fundamentally improves your product or business. 4️⃣ Hypotheses Drive Focused Innovation When everyone aligns on what you're testing, your team spends less time debating and more time building. It also fosters a culture where failure is a learning opportunity, not a setback. Share your hypotheses openly with stakeholders to align expectations and ensure everyone understands what success or failure means. 5️⃣ Iterate, Don’t Abdicate Testing your hypothesis is just the start. Use the insights gained to refine and adapt. Some of the best product breakthroughs happen not because the first hypothesis was correct but because the team iterated on what they learned.

For Every Researcher: When Should I Use Research Questions and When Should I Use Hypotheses? Key Differences, Proper Uses, and Common Mistakes Many master’s and doctoral students face a real challenge when preparing their research proposals, especially in distinguishing between research questions and hypotheses. This distinction is not a mere formality; it is a methodological decision that directly affects the strength of the study, the type of research design, and the statistical analysis employed. First: Research Questions Research questions are scientific inquiries that the researcher seeks to answer through data collection and analysis. They are often used in descriptive or exploratory studies, where the aim is to understand or describe a phenomenon without assuming prior results. Good research questions are clear, specific, directly linked to the research problem, and answerable through data. For example: What is the level of job satisfaction among high school teachers? This question focuses on description and understanding rather than testing a relationship. Second: Research Hypotheses Hypotheses are temporary scientific expectations proposed by the researcher, expressing a predicted relationship between two or more variables, tested statistically. They are commonly used in experimental or quasi-experimental studies, where the researcher has a theoretical foundation or prior studies that justify prediction. A good hypothesis is specific, testable, and derived from previous literature. Example: There are statistically significant differences in job satisfaction levels attributed to years of experience. Here, the researcher predicts a specific outcome to be tested. Key Differences - Research questions: Open-ended inquiries aimed at exploration and understanding. - Hypotheses: Declarative statements expressing a testable prediction. - Questions do not assume prior results, while hypotheses are built on expected outcomes. When to Use Each - Use research questions when studying a new phenomenon, in descriptive studies, or when lacking a strong theoretical framework. - Use hypotheses when sufficient prior studies exist, in experimental designs, or when testing relationships between variables. Combining Both It is possible to use main research questions followed by sub-hypotheses, provided there is a clear methodological justification. However, repeating the same idea in both forms without necessity is discouraged. Common Mistakes Frequent errors include using hypotheses in purely descriptive research, formulating questions that imply expected results, or failing to align questions/hypotheses with the chosen methodology. Such mistakes often lead to supervisor comments or rejection of the proposal. Distinguishing between research questions and hypotheses is a fundamental step in preparing a strong research plan.

Step-by-Step Guide: How to Craft a Strong Research Hypothesis Features of an Effective Hypothesis? 1-Testabilityobservable results. 2-Brevity and Objectivity . 3-Clarity and Relevance type of hypothesis 1-Null Hypothesis (H₀) – States that there is no relationship or effect between variables, serving as the default assumption. 2-Alternative Hypothesis (H₁) – Suggests that there is a significant relationship or effect between variables, opposing the null hypothesis. 3-Simple Hypothesis 4-Complex Hypothesis 5-Directional Hypothesis 6-Statistical Hypothesis 7-Empirical Hypothesis 8-Causal Hypothesis 9-Associative Hypothesis 10-Relational Hypothesis 11-Logical Hypothesis How to Develop a Research Hypothesis? 1. Identify the Research Problem Define a clear research question based on gaps in literature, observations, or theoretical concerns. Example: Does regular exercise improve mental health among university students? 2. Review Existing Literature Examine past research on exercise and mental health to understand existing theories, findings, and research gaps. Example: Studies suggest physical activity reduces stress and anxiety, but there is limited research on its impact on university students. 3. Specify Variables Identify the independent (cause) and dependent (effect) variables. Independent Variable (IV): Regular exercise Dependent Variable (DV): Mental health (measured by stress and anxiety levels) 4. Formulate a Hypothesis Develop a clear, testable statement predicting the relationship between the variables. Example: University students who engage in regular exercise will experience lower levels of stress and anxiety compared to those who do not exercise regularly. 5. Consider Alternative Hypotheses Identify other possible explanations for the observed relationship. Example: Other factors like diet, sleep patterns, or social support might also influence mental health. 6. Ensure Testability Check if the hypothesis can be tested using empirical methods such as surveys, experiments, or statistical analysis. Example: A survey can measure students’ exercise frequency and their reported stress and anxiety levels. 7. Write and Refine Clearly state the hypothesis and refine it for clarity, specificity, and conciseness. Refined Hypothesis: Students who engage in at least 150 minutes of moderate exercise per week will report significantly lower stress and anxiety levels than those who exercise less frequently. 8. Seek Feedback Discuss the hypothesis with peers, mentors, or experts to refine its clarity and feasibility. 9. Finalize the Hypothesis Ensure the hypothesis aligns with research objectives and provides a clear direction for the study. 10. Revisable A hypothesis is not a conclusion but a tentative assumption or prediction that guides the research process. It should be open to revision based on the study's findings.

Formulating the Research Question and Framing the Hypothesis 🎯🔍 OnlineClassHelp.Net A well-structured research question and hypothesis form the backbone of any scientific study 🏗️📚. They provide clarity, direction, and focus, ensuring the research remains systematic and goal-oriented 🎯. This article highlights the key aspects of developing a strong research question and framing a hypothesis that can be tested effectively. 🧐 Why is a Research Question Important? A clear and focused research question: ✅ Defines the study’s purpose 🎯 ✅ Establishes the variables under investigation ⚖️ ✅ Ensures feasibility within available time and resources ⏳💰 ✅ Guides data collection and analysis 📊 To formulate a strong research question, researchers must: 📖 Conduct a literature review to identify knowledge gaps 🔎 🔢 Define key variables and parameters 📏 🎯 Ensure the question is specific and researchable 📌 Types of Research Questions 📊 Different research questions serve distinct purposes: 📋 Descriptive – What are the leading causes of workplace stress? 🤯 ⚖️ Comparative – How does remote work compare to in-office work in terms of productivity? 💼🏡 🔄 Causal – Does caffeine intake improve focus and concentration? ☕🧠 🔬 Framing the Hypothesis: The Researcher’s Prediction 🔮 A hypothesis is a statement that predicts the relationship between variables 🎯. It must be: ✔️ Clear and testable ✅ ✔️ Based on prior research or theory 📖 ✔️ Capable of being validated through data collection 📊 📌 Types of Hypotheses 🤔 1️⃣ Null Hypothesis (H₀) – No relationship exists ❌ Example: "There is no significant difference in memory retention between students who study at night and those who study in the morning." 💤🌅 2️⃣ Alternative Hypothesis (H₁) – A relationship exists ✅ Example: "Students who study in the morning have better memory retention than those who study at night." ☀️📚 🚀 Steps to Formulating a Strong Hypothesis 🏆 📌 Identify the research problem 🤔 📖 Review relevant literature 🔎 ⚖️ Define independent & dependent variables 🎯 ✍️ Write a clear, testable statement 📝 📊 Collect data & test the hypothesis 🔬 💡 Key Takeaways 💡 ✅ A good research question is clear, focused, and answerable 🎯 ✅ Hypotheses guide scientific investigation and must be testable 🧪 ✅ Research questions can be descriptive, comparative, or causal 📊 ✅ A null hypothesis (H₀) assumes no effect, while an alternative hypothesis (H₁) predicts a relationship 🔄 ✅ Well-formulated hypotheses streamline research and lead to valid conclusions 🔬📚 #ResearchMethodology 📖 #HypothesisTesting 🔬 #ScientificInquiry 🧐 #AcademicWriting ✍️ #DataAnalysis 📊 #ResearchFramework 🏗️ #EmpiricalResearch 📚 #Variables ⚖️ #StudyDesign 🎯 #NullHypothesis ❌ #AlternativeHypothesis ✅ #ScientificMethod 🏆 #CausalResearch 🔄 #DescriptiveResearch 📋 #ComparativeResearch ⚖️ #TestingHypotheses 🧪 #DataDriven 📉 #QuantitativeResearch 🔢 #QualitativeResearch 🗣️ #ThesisWriting 📜

🚀 𝐓𝐡𝐞 #𝟏 𝐑𝐞𝐚𝐬𝐨𝐧 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐅𝐚𝐥𝐥𝐬 𝐀𝐩𝐚𝐫𝐭 (𝐚𝐧𝐝 𝐇𝐨𝐰 𝐭𝐨 𝐀𝐯𝐨𝐢𝐝 𝐈𝐭) Most research doesn’t fail because of poor data collection. It fails long before that — at the moment the research question is written. After reviewing this article on crafting research questions and hypotheses, one thing is clearer than ever: 👉 𝐴 𝑠𝑡𝑢𝑑𝑦 𝑖𝑠 𝑜𝑛𝑙𝑦 𝑎𝑠 𝑔𝑜𝑜𝑑 𝑎𝑠 𝑡ℎ𝑒 𝑞𝑢𝑒𝑠𝑡𝑖𝑜𝑛 𝑖𝑡’𝑠 𝑏𝑢𝑖𝑙𝑡 𝑜𝑛. Here are the most powerful takeaways researchers, grad students, and evidence-based practitioners need to know: 1️⃣ 𝑮𝒓𝒆𝒂𝒕 𝒓𝒆𝒔𝒆𝒂𝒓𝒄𝒉 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏𝒔 𝒂𝒓𝒆 𝒇𝒐𝒄𝒖𝒔𝒆𝒅, 𝒔𝒑𝒆𝒄𝒊𝒇𝒊𝒄, 𝒂𝒏𝒅 𝒑𝒖𝒓𝒑𝒐𝒔𝒆𝒇𝒖𝒍 Vague questions lead to vague findings. A strong research question: ✔ Is clear about the population, variables, and context ✔ Comes from a deep understanding of current literature ✔ Points directly to what the study must investigate As the authors emphasize, unfocused questions = unreliable outcomes. 2️⃣ 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒆𝒔 𝒂𝒄𝒕 𝒂𝒔 𝒕𝒉𝒆 "𝒄𝒐𝒎𝒑𝒂𝒔𝒔" 𝒐𝒇 𝒕𝒉𝒆 𝒔𝒕𝒖𝒅𝒚 A hypothesis isn’t just a guess — it’s a formal, evidence-backed prediction. Strong hypotheses are: 𝑇𝑒𝑠𝑡𝑎𝑏𝑙𝑒, 𝐸𝑡ℎ𝑖𝑐𝑎𝑙, 𝐿𝑜𝑔𝑖𝑐𝑎𝑙, and 𝐺𝑟𝑜𝑢𝑛𝑑𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒𝑜𝑟𝑦 𝑜𝑟 𝑝𝑟𝑖𝑜𝑟 𝑒𝑣𝑖𝑑𝑒𝑛𝑐𝑒. When research questions are weak, hypotheses become impossible to verify… and the entire study loses direction. 3️⃣ 𝑸𝒖𝒂𝒏𝒕𝒊𝒕𝒂𝒕𝒊𝒗𝒆 𝒗𝒔. 𝑸𝒖𝒂𝒍𝒊𝒕𝒂𝒕𝒊𝒗𝒆 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏𝒔 ≠ 𝒕𝒉𝒆 𝒔𝒂𝒎𝒆 𝒕𝒉𝒊𝒏𝒈 One of the biggest mistakes? Trying to write qualitative questions like quantitative ones — or vice versa. Different questions → different hypotheses → different study designs. 4️⃣ 𝑼𝒔𝒆 𝒇𝒓𝒂𝒎𝒆𝒘𝒐𝒓𝒌𝒔 — 𝒏𝒐𝒕 𝒈𝒖𝒆𝒔𝒔𝒘𝒐𝒓𝒌 The authors recommend validated frameworks like: 𝐹𝐼𝑁𝐸𝑅 (𝐹𝑒𝑎𝑠𝑖𝑏𝑙𝑒, 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑖𝑛𝑔, 𝑁𝑜𝑣𝑒𝑙, 𝐸𝑡ℎ𝑖𝑐𝑎𝑙, 𝑅𝑒𝑙𝑒𝑣𝑎𝑛𝑡), 𝐹𝐼𝑁𝐸𝑅𝑀𝐴𝑃𝑆, 𝑃𝐼𝐶𝑂𝑇 (𝑓𝑜𝑟 𝑐𝑙𝑖𝑛𝑖𝑐𝑎𝑙/𝑞𝑢𝑎𝑛𝑡𝑖𝑡𝑎𝑡𝑖𝑣𝑒), & 𝑃𝐸𝑂 (𝑐𝑜𝑚𝑚𝑜𝑛 𝑖𝑛 𝑞𝑢𝑎𝑙𝑖𝑡𝑎𝑡𝑖𝑣𝑒 𝑖𝑛𝑞𝑢𝑖𝑟𝑦) Good researchers don’t "wing it." They engineer their questions. 5️⃣ 𝑹𝒆𝒔𝒆𝒂𝒓𝒄𝒉 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏 → 𝒉𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒊𝒔 → 𝒐𝒃𝒋𝒆𝒄𝒕𝒊𝒗𝒆𝒔 → 𝒔𝒕𝒖𝒅𝒚 𝒅𝒆𝒔𝒊𝒈𝒏 This sequence matters. Change the order, and the study collapses. Strong research flows like this: 1. Review the literature 2. Identify the knowledge gap 3. Write a clear research question 4. Develop testable hypotheses 5. Define objectives 6. Choose methods that align with all of the above It’s an elegant chain, but only if you build it properly. -------------------------------------------------------------------------------------- ✅ 𝐼𝑓 𝑦𝑜𝑢'𝑟𝑒 𝑝𝑟𝑒𝑝𝑎𝑟𝑖𝑛𝑔 𝑎 𝑡ℎ𝑒𝑠𝑖𝑠, 𝑑𝑒𝑠𝑖𝑔𝑛𝑖𝑛𝑔 𝑎 𝑠𝑡𝑢𝑑𝑦, 𝑜𝑟 𝑚𝑒𝑛𝑡𝑜𝑟𝑖𝑛𝑔 𝑛𝑒𝑤 𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ𝑒𝑟𝑠, 𝑖𝑛𝑣𝑒𝑠𝑡 𝑡𝑖𝑚𝑒 ℎ𝑒𝑟𝑒. 𝐼𝑡’𝑠 𝑛𝑜𝑡 𝑜𝑝𝑡𝑖𝑜𝑛𝑎𝑙 — 𝑖𝑡’𝑠 𝑡ℎ𝑒 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑎 𝑝𝑢𝑏𝑙𝑖𝑠ℎ𝑎𝑏𝑙𝑒 𝑠𝑡𝑢𝑑𝑦 𝑎𝑛𝑑 𝑎 𝑑𝑒𝑎𝑑 𝑒𝑛𝑑.

How great hypothesis help you learn fast, and pick the right path. At Moonpig, I’ve been thinking a lot about how we can make testing more purposeful — not just to move metrics and see what happened, but to deeply understand customer behaviour, and why customers do what they do. So I recently introduced a 💡 Hypothesis Framework 💡 to help our product teams write better, bolder hypotheses, that help us understand the most important things about our customers. The goal? 1️⃣ Learn faster (through various means, both quant and qual) 2️⃣ Take bigger (but smarter) risks where it matters most 3️⃣ Focus on testing to learn, not just to prove 4️⃣ And shift from vague test ideas to more opinionated bets that help us pick a direction We want teams to ask themselves: “What would we learn about our customers if this fails?” and “what will I do differently based on the outcome of this test?” Because every test should move us forward — regardless of outcome… meaning we never ‘fail’, we always learn. The hypothesis canvas I created guides teams to define... 🧠 What they believe to be true about why our customers do what they do 🧪 How they will validate their assumption quickly (either through research, or testing) 📈 What they will measure (thinking about leading UX metrics such as engagement or clicks) 🤩 How they’ll know they are right (what % users need to agree or exhibit the desired behaviour) 🗺️ What they’ll do next based on what they have learned Finally, it’s important to remember that hypothesis aren’t created after you come up with the idea, they are created before. Hypothesis are tools to help you generate ideas to prove your assumptions true or false, ways to learn about your customers, not just to prove that your idea worked. #ProductDesign #ProductManagement #Experimentation #HypothesisDrivenDesign #Moonpig #innovation #uxdesign

Failing to start with a good hypothesis is like trying to ride a bike with no wheels. You're not going to get very far. Or you might drag it along. And if you do ever arrive at your destination you won't understand how you got there. 🚲 I like the following structure; I know that...I believe that...Will result in...because ❓ "I know that" is grounded in data, you''ve got insight from research or past tests results supporting your hypothesis. ❓ "I believe that" is about stating your assumption, your belief, the testable thing. ❓ "Will result in" this part makes you specify what you think will happen and how you will measure it ❓ "Because" connects your insight, change and result to the user behaviour. Here is an example: I know that users have a 66% drop off rate on the form page. User testing showed users experience frustration at the amount of fields to fill in. I believe that reducing down the number of fields on the form will result in an increase in CVR. Because the users will experience less cognitive load. Ultimately your experiment is only as strong as your hypothesis so utilising a framework like this brings rigour and structure. Are there other frameworks you recommend? #hypothesis #cro #experimentation #framework #getitright