The document outlines a series of webinars focused on deep tech startups and the application of the scientific method in startup development. It emphasizes the importance of value and growth hypotheses, providing examples from various sectors such as biotech, health tech, and agricultural tech, along with strategies for testing these hypotheses through minimum viable products and iterative learning. Additionally, it discusses the need for solutions to be adopted by relevant communities to create a significant impact on society.

1. Other webinars in this series
• Feb 2024: Conceptualize Your Deep
Tech Startup Idea: Learn Your Way
Through AI State of the Art
• Apr 2024: Teamwork in a Startup:
Team Building, Managing Talent
• Oct 2024: Creativity in Problem-
Solving and Decision-Making
• Jan 2025: Receiving Feedback &
Pivoting in a Startup, How to Grow
Learn more about EURAXESS Startup Hub

2. Discovery and validation with
scientific method
The Lean Scientific
Startup approach
dr Milan Zdravković, EURAXESS Startup Hub

3. What is scientific
method?
• The scientific method is a
systematic and logical
approach used by scientists to
investigate and understand
natural phenomena, acquire
new knowledge, and develop
and prove theories
(explanations) about the
world around us.
• It is a structured process that
aims to minimize bias, errors,
and subjectivity in the pursuit
of reliable and verifiable
information.

4. Key steps of a scientific method
Observation Question Research Hypothesis
Experimentation Data Collection Analysis Conclusion
Communication Peer Review
Iteration and
Refinement

5. Hypotheses and variables - examples
“Machine learning model trained with
historical market data and news sentiment
can predict short-term stock price
movements more accurately than traditional
technical indicators.”
Independent variable (the ones that scientist change during
the experiment): ML model, training data period, selection
of news sources, method for sentiment classification
Dependent variable (the ones that are being changed and
measured): improvement of accuracy over traditional
technical indicators
Control variables: choice of technical indicators, accuracy
metrics
“Inhibiting a specific enzyme identified in a
disease pathway will lead to a reduction in
disease symptoms in vitro and in vivo.”
Independent variable: dosage and concentration of the
inhibitor
Dependent variable: reducing disease symptoms
Control variables: conditions and duration of the
experiment, the methods used to measure disease
symptoms

6. Let’s now get moving off science (are we
moving off really?)
Scientific method is
focused on problem
solving.
But solving problems does not
necessary saves the world.
Solution needs to be adopted by the
relevant community in order to have
an impact.
Who benefits from
confirming the scientific
hypotheses?
Individuals (investors, traders,
patients) and society at large.
Companies who transform the
scientific result into innovative
product or service.
From problem-focused
to beneficiary-focused
hypotheses:
The Value Hypothesis addresses the
question of whether a product or
service delivers value to potential
customers.
The Growth Hypothesis addresses the
question of how the startup will
acquire and retain customers at scale.

7. Examples
of
value
hypotheses
for
scientific
startups
Biotech Startup: "Our drug
discovery platform will reduce the
time required for identifying
potential drug candidates by at
least 50%, leading to a 30%
increase in R&D efficiency for
biopharmaceutical companies."
Health Tech Startup: "Our AI-
powered diagnostic tool will
increase the accuracy of early
disease detection by 20%,
resulting in a 15% reduction in
patient treatment costs and a 25%
improvement in patient
outcomes."
Agricultural Tech Startup: "Our
precision agriculture solution will
optimize water and nutrient
usage, leading to a 15% increase
in crop yields and a 30% reduction
in resource consumption for
farmers."
Energy Tech Startup: "Our
predictive maintenance solution
will reduce unplanned equipment
downtime by 40%, resulting in a
25% decrease in maintenance
costs and a 20% increase in
equipment efficiency for energy
companies."
Environmental Startup: "Our real-
time pollution monitoring
technology will reduce general
population response times to
pollution events by 50%, leading
to a 15% decrease in pollution-
related health issues and a 20%
improvement in air quality."
Materials Science Startup: "Our
advanced material will offer a
30% improvement in strength-to-
weight ratios compared to
traditional materials, leading to a
10% increase in product lifespan
and a 15% reduction in
maintenance costs for
manufacturers."

8. Examples
of
growth
hypotheses
for
scientific
startups
Biotech Startup: "By the end of
the year, we aim to increase our
partnerships with
biopharmaceutical companies by
50%, resulting in a 30% growth in
revenue from collaborative
research projects."
Health Tech Startup: "Within six
months, we expect to expand our
user base by 100%, resulting in a
40% increase in monthly
subscription revenue for our AI-
powered telehealth platform."
Agricultural Tech Startup: "Over
the next year, we plan to onboard
200 new farms onto our precision
agriculture platform, leading to a
60% growth in our annual
recurring revenue."
Energy Tech Startup: "Within nine
months, we aim to secure
contracts with five major energy
companies, driving a 50% increase
in our sales pipeline and a
projected 35% growth in
revenue."
Environmental Startup: "Over the
next quarter, we anticipate
expanding our real-time pollution
monitoring technology to three
new cities, resulting in a 40%
increase in the number of
monitoring units deployed."
Materials Science Startup: "By
the end of the fiscal year, we
project to have our advanced
material integrated into products
by 10 new manufacturers,
contributing to a 25% increase in
material sales."

9. Value hypothesis
testing
Growth hypothesis
testing
The Lean Startup: Build-measure-learn loop
Design hypotheses and build
Minimum Viable Product (MVP)
Continuous deployment
Split (A/B) testing
Pivot or
persevere?
Iterative
validated
learning
Innovation
accounting
Customer
development

10. Examples of MVPs of scientific startups
Biotech Startup
Developing a
novel Drug:
MVP: A small-scale,
in vitro version of the
drug candidate with
basic testing
capabilities.
Purpose: Test the
drug's effectiveness
in a controlled
laboratory setting,
assess its interaction
with target
molecules, and
gather preliminary
data on its potential
efficacy.
Health Tech
Startup Creating
a Medical
Device:
MVP: A functional
prototype of the
medical device with
core features.
Purpose: Test the
device's usability,
accuracy, and safety
in a controlled
environment,
potentially involving
simulated scenarios
or volunteer subjects.
Agricultural
Startup Crop
Monitoring
Solution:
MVP: A basic sensor
system to monitor
soil moisture and
nutrient levels in a
small plot.
Purpose: Validate
whether the sensor
technology can
accurately measure
critical agricultural
parameters and
provide data that can
inform irrigation and
fertilization decisions.
Energy Startup
Renewable
Energy Solution:
MVP: A small-scale
prototype of the
renewable energy
device, such as a
solar panel or wind
turbine.
Purpose: Test the
device's efficiency,
durability, and energy
generation capacity
under controlled
conditions to validate
its feasibility.
Environmental
Startup
Studying
Pollution Levels:
MVP: A limited
deployment of
sensors in a specific
area to measure air
or water pollution
levels.
Purpose: Collect data
on pollution levels,
analyze trends, and
assess the
effectiveness of the
sensor technology in
a real-world
environment.
Materials
Science Startup
Creating New
Material:
MVP: A small sample
of the novel material
synthesized for initial
testing.
Purpose: Evaluate the
material's properties,
such as strength,
conductivity, or
flexibility, to
determine if it
exhibits the desired
characteristics.

11. Integrated lean scientific startup method
Observation Question
Research Hypothesis
Experimentation Data Collection
Analysis
Conclusion
Communication
Peer Review
Iteration and
Refinement
Market research,
competitors
Value and growth
hypotheses
MVP Pivot or persevere
Hypotheses testing,
split testing
Customer
development
Iterative validated
learning

12. Innovative business
development is
evidence based and it
closely adheres to
scientific methodology
Responsible Research
and Innovation is
customer focused and
impact driven
What can two worlds learn one from another?