Introduction
In the world of innovation, entrepreneurship, and digital transformation, one question echoes daily across startups, large corporations, innovation laboratories, and boardrooms: “Is this idea really worth it?” Although it sounds simple, this is one of the hardest questions to answer accurately. Ideas are abundant, but resources — time, capital, people, and reputation — are scarce.
This is where three fundamental concepts for structured and efficient innovation with reduced risk come into play:
- Hypothesis
- MVP (Minimum Viable Product)
- Minimum Criteria for Success (MCS)
Together, these three elements form the backbone of modern business experimentation. They connect science, product, strategy, data, and decision-making. Far from being mere startup buzzwords, they represent a profound shift in how organizations learn from the market.
This article explores, in a practical and strategic way:
- What a hypothesis is and why it is essential
- How to transform assumptions into testable hypotheses
- The role of the MVP as an experimentation tool
- What the Minimum Criteria for Success (MCS) is
- How to define validation metrics
- How startups and companies set their success criteria
- How to analyze cost versus reward before scaling a product
What Is a Hypothesis?
A hypothesis is a clear, objective, testable, and measurable statement about something you believe to be true, based on assumptions that have not yet been validated.
Simply put:
A hypothesis is a structured bet you make about customer behavior, the market, or your product.
In the scientific method, every experiment starts with a hypothesis. The researcher believes that something will occur under certain conditions and then designs an experiment to test that belief. The same principle applies to business and innovation.
Formal Definition
A hypothesis can be defined as:
“A single, written, specific, and testable statement of what you believe to be true in relation to the assumptions you have identified.”
It is not a loose guess or a subjective opinion. It is a proposition that will be confirmed or refuted by real data.
From Assumptions to Hypotheses: The First Step of Innovation
Every new idea is born from assumptions. We assume that:
- A relevant problem exists
- A specific group of people suffers from this problem
- They are willing to pay for a solution
- Our solution solves this problem better than existing alternatives
These assumptions are dangerous when left untested. Many companies fail not because of poor execution, but because they start from incorrect assumptions.
Example of an assumption:
“People want an app to organize tasks.”
Example of a hypothesis:
“People aged 25 to 40 who work remotely are willing to pay $5 per month for an app that automates their task management.”
A hypothesis transforms a vague idea into something specific, measurable, and testable.
The Hypothesis as the Foundation of the MVP
The MVP (Minimum Viable Product) is often misunderstood. Many believe it is an “unfinished” or “overly simplified” product. In reality, the MVP is a learning experiment, not a final product.
The MVP exists to answer one central question:
“Is my hypothesis true or false?”
Therefore, before thinking about features, design, or code, it is essential to answer:
- Which hypothesis am I testing?
- What user behavior do I need to observe?
- Which metric will indicate validation?
An MVP Is Not Just Technology
A common mistake is to associate the MVP only with software. In practice, an MVP can be:
- A landing page
- A form
- A demonstration video
- A waiting list (“coming soon”)
- A clickable prototype
- A manual service disguised as automation (concierge MVP)
What matters most is not the MVP’s format, but its ability to test the hypothesis at the lowest possible cost.
Why Having a Hypothesis Is Essential
Having a hypothesis is what turns a project into a controlled experiment. Without a hypothesis, there is no structured learning — only disorganized trial and error.
A well-defined hypothesis allows you to:
- Clearly define what will be tested
- Choose the right metrics
- Reduce emotional bias
- Avoid wasting resources
- Make data-driven decisions
Just like in science, innovating without a hypothesis is like running experiments without a method: results may appear, but they are difficult to interpret.
The Three Possible Outcomes of an MVP
Every MVP experiment can lead to only three outcomes:
- The hypothesis is false
- The hypothesis is true
- The result is inconclusive (somewhere in the middle)
In practice, the third option is the most common. It is estimated that around 90% of MVP experiments fall into this middle zone, neither fully validating nor completely invalidating the initial hypothesis.
This is not a problem — on the contrary, it is the normal pattern of learning. Innovation rarely happens in binary leaps. It occurs through successive adjustments.
What Is the Minimum Criteria for Success (MCS)?
The Minimum Criteria for Success (MCS) is the minimum performance threshold that an MVP must reach for the continuation of the project to make sense.
In other words, the MCS answers the question:
“How much is enough to decide to move forward?”
Without a defined MCS, any result can seem “good enough,” depending on the team’s emotional expectations. The MCS brings objectivity, clarity, and discipline to decision-making.
Why the MCS Is So Important
The MCS helps avoid three common traps:
- Scaling a bad idea due to excessive optimism
- Abandoning a promising idea due to impatience
- Interpreting data in a biased way to justify decisions already made
It forces a rational analysis of cost versus benefit before major investments occur.
How to Define the Minimum Criteria for Success
Defining the MCS requires consideration of two fundamental pillars:
1. Customer Interest Metrics
You must choose metrics that demonstrate real interest, not just vanity metrics.
Examples include:
- Conversion rate
- Number of sign-ups
- Waiting list size
- Retention rate
- Shares
- Purchase intention
- Free trials started
- Payments completed
These metrics must signal that the problem is real and the solution is desired.
2. Cost and Reward Analysis
It is essential to calculate the full cost of the experiment, including:
- Development time
- Team working hours
- Infrastructure costs
- Marketing and advertising expenses
- Opportunity cost
- Brand impact
- Team burnout
- Distraction from other strategic projects
At the end, the core question is simple:
Is the potential return greater than the cost to achieve it?
If the answer is “no,” the idea may be interesting, but it is not economically sustainable.
The Turning Point: When Benefit Exceeds Cost
The real goal of the MCS is to identify the minimum point at which benefits begin to outweigh costs.
This point may be:
- A minimum number of users
- A minimum monthly revenue
- A minimum engagement level
- A proven reduction in costs
- A minimum satisfaction indicator
Before reaching this point, the investment is still a bet. After it, it becomes a rationally justified opportunity.
Minimum Success Criteria in Startups
Startups, in particular, operate under high risk. They have no guaranteed market, no consolidated brand, and no predictable cash flow. Therefore, their success criteria tend to be more focused on problem validation than on immediate profit.
Most Common Startup Validation Metrics
- Landing page conversion rate
- Number of emails collected
- Weekly user growth
- Retention after 7, 30, and 90 days
- Recurring product usage
- Engagement (DAU/MAU)
- Net Promoter Score (NPS)
- Monthly Recurring Revenue (MRR)
These metrics validate something essential:
Is there a real problem, and do people truly care about the solution?
MVPs as “Coming Soon” Pages and Simulated Products
In practice, many MVPs are not full products. They are presentations of the product as if it already existed.
Common formats include:
- Landing pages with a purchase button
- Waiting lists
- Demo videos
- Interactive prototypes
- Manually operated products
- Service simulations
This allows you to test the hypothesis before writing a single line of code.
The Relationship Between Hypothesis, MVP, and MCS
These three concepts form a continuous cycle:
- You create hypotheses
- You test them with MVPs
- You assess results using the MCS
- You learn, adapt, or pivot
- You create new hypotheses
This is how modern organizations learn from the market in a structured way.
Why 90% of MVPs End Up “In the Middle”
Rarely is a hypothesis fully confirmed or totally rejected in the first test. Most of the time, results show that:
- The audience exists, but not as expected
- The problem exists, but is less urgent
- The solution generates interest, but not at the expected price
- The acquisition channel is not the right one
These insights enable fine-tuning that significantly increases the chance of success in the next iteration.
Popularity Does Not Mean Economic Viability
One of the most dangerous mistakes in the startup world is confusing engagement with financial sustainability.
A product may have:
- Many users
- Many likes
- Many free sign-ups
And still:
- Generate insufficient revenue
- Have very high acquisition costs
- Show high churn
- Have an unviable monetization model
This is why the MCS must always consider:
Popularity without economic viability does not sustain a business.
Practical Application in Traditional Companies
These concepts do not apply only to startups. Established companies increasingly use:
- MVPs for new products
- Hypotheses for new digital channels
- MCS for innovation initiatives
- A/B tests for strategic decisions
The difference is that, in large organizations, costs and reputational risks are even higher — making methodological rigor even more critical.
Integrated Practical Example
Imagine a hospital company that wants to create an app for bed management.
Assumptions:
- Current management is inefficient
- Professionals want more automation
- The app will reduce average admission time
Hypothesis:
“Automating bed management will reduce the average waiting time for admission by 15% in medium-sized hospitals.”
MVP:
- Functional prototype in one pilot unit
- Hybrid operation (manual + digital)
Metrics:
- Average waiting time
- Occupancy rate
- User satisfaction
MCS:
- Minimum 10% reduction in admission time
- Minimum 70% staff adoption
If results exceed these thresholds, the product is worth scaling.
The Role of Organizational Culture
None of these concepts work without a culture that:
- Accepts experimentation
- Tolerates controlled failure
- Values data over opinions
- Encourages continuous learning
- Avoids punishing well-structured failures
Companies with a punitive culture inhibit MVPs and hypothesis testing — and therefore inhibit innovation itself.
Conclusion
Innovation has shifted from an intuitive act to a structured learning process based on hypotheses, experiments, and clear success criteria.
Well-formulated hypotheses prevent waste.
Well-designed MVPs accelerate learning.
Minimum success criteria bring discipline.
The right metrics turn data into decisions.
In a world of scarce resources, those who learn faster will win — not those who make fewer mistakes. The true competitive advantage lies in the ability to test, learn, adapt, and scale with method, economic awareness, and a focus on real value creation.