Sujay Rao Mandavilli IJISRT25AUG564 minimizing......pdf

Volume 10, Issue 8, August – 2025 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/25aug564
IJISRT25AUG564 www.ijisrt.com 723
Minimizing the Time Taken Between Hypothesis
Generation, Hypothesis Testing and Refinement:
A Necessary Adjunct in the Epoch of Fast-Paced
Science
Sujay Rao Mandavilli
Publication Date: 2025/08/21
Abstract: The objective of this paper is to emphasize the need for minimizing the time taken between hypothesis formulation or
hypothesis generation, hypotheses testing and hypothesis refinement, and the role it can definitely play in accelerating scientific
progress. We begin this paper by defining what a hypothesis is, delineating the different steps involved in the formulation of a
hypothesis, evaluating the different types of hypotheses, and also by distinguishing and differentiating hypotheses, theories and
laws. We also then visit the concept of a grounded theory, and explore the types, uses, and limitations of grounded theory. We
also then revisit the concept of latency periods, a concept that we had discussed and debated previously, and to show how time
crashing techniques can be suitably employed to bring down timeframes in the entire research cycle. We also then provide and
furnish a large number of examples from various fields in the social sciences to illustrate how many hypotheses remain untested
for long periods in time, or remain dangling in mid-air. We do then hope, anticipate and expect that this will prove to be an
important paper in twenty-first century science, and will encourage and embolden researchers to take up hypotheses verification
and hypothesis ratification studies in large numbers in the not too distant future.
How to Cite: Sujay Rao Mandavilli (2025) Minimizing the Time Taken Between Hypothesis Generation, Hypothesis Testing and
Refinement: A Necessary Adjunct in the Epoch of Fast-Paced Science. International Journal of Innovative Science and Research
Technology, 10(8), 723-726. https://doi.org/10.38124/ijisrt/25aug564
I. INTRODUCTION
The objective of this paper is to emphasize the need for
minimizing the time taken between hypothesis formulation or
hypothesis generation, hypotheses testing and hypothesis
refinement, and the role it can definitely play in accelerating
scientific progress. We begin this paper by defining what a
hypothesis is, delineating the different steps involved in the
formulation of a hypothesis, evaluating the different types of
hypotheses, and also by distinguishing and differentiating
hypotheses, theories and laws. We also then visit the concept of
a grounded theory, and explore the types, uses, and limitations
of grounded theory. We also then revisit the concept of latency
periods, a concept that we had discussed and debated
previously, and to show how time crashing techniques can be
suitably employed to bring down timeframes in the entire
research cycle. We also then provide and furnish a large number
of examples from various fields in the social sciences to
illustrate how many hypotheses remain untested for long
periods in time, or remain dangling in mid-air. We do then hope,
anticipate and expect that this will prove to be an important
paper in twenty-first century science, and will encourage and
embolden researchers to take up hypotheses verification and
hypothesis ratification studies in large numbers in the not too
distant future. Theories must also be tested across space and
time, and we had also proposed diachronic synchronic studies
previously. Our paper also highlights the importance of metric
and performance measurement measures for scientific
processes in general in contemporary, twenty-first century
science. This is one area researchers must work towards in the
years and decades to come, given that progress in this area is
deficient and insufficient.
 What is a Hypothesis?
The word hypothesis is thought to have been derived from
the following two words, namely: “hypo” and “thesis” which is
equal to “Hypothesis”. The term “hypo” means something
which is tentative or subject to reverification and “thesis” which
means a statement pertaining to a solution of a problem.
A hypothesis may therefore be taken to mean a
proposed explanation for an observed phenomenon.
A hypothesis must always be based on methodologically and
systematically carried out observations over a period in time.

Hypotheses must also make testable and reproducible
prediction about real-world phenomena, generally beginning
with a hunch or an educated thought, but progressively refined
as and when additional data becomes available. Hypotheses,
after constant verification and repeated observations, morph
into theories, which are further processed with additional data
to become incontestable laws. To generate a hypotheses, we
must collect as much data and information about a given area
of study. After data collection, we must corroborate the data
through additional information, and look for potential
underlying causes for the problem. We may also generate a set
of parallel hypotheses, and prove or disprove some of them as
necessary. Therefore, a working hypothesis may be generated.
Hypotheses generation also typically involves the identification
and generation of independent and dependant variables or cause
and effect variables. Intervening variables may also be defined.
1 2 3 4 5 6 7
There are many different types of hypotheses in common
use, and examples of these are simple hypotheses, complex
hypotheses, directional hypotheses, non-directional
hypotheses, null hypotheses, and alternative hypotheses. A
simple hypothesis postulates a simple relationship between one
independent variable and one dependent variable alone, while
a complex hypotheses postulates a relationship with at least
three variables, perhaps even more. A directional hypothesis
indicates or specifies the direction of relationships between
variables, while a non-directional hypothesis does not. A null
hypothesis assumes there are no significant differences
between means, while an alternative hypothesis does. An
associative hypotheses proposes relationship between
variables without implying causation, while in the case of a
causal hypotheses, causation is implied. An empirical
hypothesis is based mostly or entirely on observations and
experimentation while a statistical hypotheses involves
correlation between large volumes of data. A research
1
Hempel, C. G. (1952). Fundamentals of Concept Formation
in Empirical Science. Chicago: University of Chicago Press
2 Hilborn, Ray; Mangel, Marc (1997). The ecological
detective: confronting models with data. Princeton University
Press. p. 24
3
McRaney, David (2022). How Minds Change: The Surprising
Science of Belief, Opinion, and Persuasion. New York:
Portfolio/Penguin
4
Bolsen, Toby; Druckman, James N. (2015). "Counteracting
the Politicization of Science". Journal of Communication (65):
746
5
Towards 360 degree approaches to hypothesis formulation
and evaluation: Another epochal milestone in twenty-first
century science Sujay Rao Mandavilli Published in IJISRT,
July 2025
6
Envisaging a new era in interdisciplinary and transdisciplinary
research: Presenting the COMPASS model for interdisciplinary
and transdisciplinary research Sujay Rao Mandavilli IJISRT,
June 2025
hypothesis is one which is used as a basis of further
downstream research, while a working hypothesis is a
preliminary hypothesis that is a launch pad for further
investigation. A logical hypothesis on the other hand, is one
where logic and reasoning are primarily or extensively used.
There are many distinct steps involved in the process of
hypothesis generation. In order to generate a strong and a robust
testable hypothesis, we must always begin with a research
question in a specified area of study or topic of investigation,
conduct preliminary research or preliminary investigations, and
then arrive at a testable hypothesis that clearly identifies and
establishes the relationship between variables so that they can
be tested further. Therefore, we may identify several elements
of a good hypothesis. For example, a good hypothesis must be
specific, clear, unambiguous, empirical, testable, and must also
as far as possible address real-world problems or fill gaps in
knowledge while avoiding philosophical questions or non-
scientific areas. A good hypothesis must also correlate variables
clearly, though this may not always be possible in the case of
qualitative research. There are also fundamental differences
between a hypothesis and a theory. A theory offers a better
tested and a better substantiated explanation than a hypothesis,
and is often derived from it. It therefore represents a subsequent
stage in a scientific exploration or study. A theory is then tested
further, so that it may become a law. The distinction between
all three is however not often clearly implied or established, and
they are sometimes used loosely and carelessly. 8 9 10 11 12
 What is Grounded Theory?
The idea and the general concept of a grounded theory,
we believe is an extremely important one, though it does not
appear to have been popularized the way it should be. Grounded
theory in sum, refers to a qualitative research techniques
whereby theories are systematically developed from data
collected and analyzed often in a short span of
7
Towards “Thick analysis” of statements, propositions and
assertions: Compendious evaluations with immense benefits in
research Sujay Rao Mandavilli Published in SSRN, July 2025,
IJISRT, July 2025 and elsewhere
8
Groh, Arnold (2018). Research Methods in Indigenous
Contexts. New York: Springer
9
Talja, Sanna and Pamela J. Mckenzie (2007). Editor's
Introduction: Special Issue on Discursive Approaches to
Information Seeking in Context, The University of Chicago
Press
10
Creswell, John W. (2008). Educational Research: Planning,
conducting, and evaluating quantitative and qualitative
research (3rd ed.). Upper Saddle River, NJ: Pearson
11
Kara, Helen (2012). Research and Evaluation for Busy
Practitioners: A Time-Saving Guide. Bristol: The Policy Press
12
Amplifying the importance of synchronic-diachronic
approaches in social sciences research: Unleashing the power
of this technique for better sociocultural analysis Sujay Rao
Mandavill Published in IJISRT, July 2025, SSRN, July, 2025

time. Sometimes, a more prolonged approach is adopted. This
methodology allows researchers to carefully and
methodologically collect theories based on real-world
observations and interpretations, rather than proceeding from a
pre-existing hypothesis that was developed without the use of
data. In case of grounded theory, inductive reasoning is used,
coupled with an iterative process, and theoretical sampling. A
constant comparison of different sources of data is often
employed, and cross-verification or cross-validation is often
performed. We also have the concept of theoretical saturation.
This refers to the point where additional data that was proposed
to be collected no longer provides vital clues or insights into a
given problem, and the marginal utility of collecting new and
additional data is negative. Researchers also indulge in constant
reflexivity, and retrospect on their own errors, biases and
prejudices. While grounded research may not always be
possible due to time, cost, and practical concerns and
considerations, hypotheses must be generated with some initial
data at east, preferably formally collected data. These need to
be tested and validated as quickly and as efficiently as possible,
preferably within a specified timeframe. 13 14 15
There are different types of grounded theory, such as
classic grounded theory and constructivist grounded theory.
Classic grounded theory as developed by Barney Glaser and
Anselm L. Strauss in 1967, (The discovery of grounded theory)
emphasizes the generation of abstract, formal theories. As per
this approach, the core concerns and requirements of research
participants and parties to the study are critically identified, and
factored into the research study. The research study must then
explain how the concerns of the participants are resolved.
Patterns and interrelationships between data must also be then
identified and discovered. Theoretical knowledge must be
possessed by researchers, and a constant comparison of data
and data sources carried out. Classic grounded theory also
speaks about different types of coding such as open coding,
selective coding and theoretical coding which are somewhat
outside the scope of this paper. Classic grounded theory also
emphasized processes and a data-driven approach.
Constructivist grounded theory was developed by the
sociologist Kathy Charmaz, and it focuses more on socially
constructed processes and realities. Researchers’ perspectives
and interpretations of reality are also factored into the process.
However, data-driven processes are indeed emphasized in such
a case.
13
Charmaz, Kathy (2009) 'Shifting the grounds: Constructivist
grounded theory methods', in J. M. Morse, P. N. Stern, J.
Corbin, B. Bowers, K. Charmaz and A. E. Clarke (eds.),
Developing Grounded Theory: The Second Generation. Walnut
Creek: Left Coast Press. pp. 127–154.
14
Mills, Jane, Bonner, Ann, & Francis, Karen (2006) 'Adopting
a constructivist approach to grounded theory: Implications for
Grounded theory, which is primarily used as a qualitative
research approach in the social sciences, has had to face several
important and significant criticisms. These include time
considerations, cost considerations, and researcher interest or
patience. It is also often demanding in nature, is accompanied
by potential for researcher bias, and is associated with
unresolved issues in theoretical and methodological
frameworks. There are different versions and variants of it, and
the core concepts may often be at variance with each others.
Some argue that such an approach is not even necessary, and
reliable hypotheses can be formulated with minimal data as
long as sound logic and reasoning is used. Hypotheses can be
tested at later points is time, as and where more data becomes
available, or when there is additional research funding. We do
accept the limitations of grounded theory, though decisions
must always be taken on a case to case basis. Even if hypotheses
are formulated based on limited data (including scenarios where
only limited data is available when hypotheses are constructed),
they must be ratified and tested adequately, thoroughly and
completely as and when more and more data or information
becomes available. This may be accomplished as a concerted
activity either by the researcher involved or in question, or by
another researcher or teams or groups of researchers.
 Latency Period
We had written extensive about latency period earlier, and
had even devoted and dedicated large sections of a paper to it.
The terms is widely used in communications science, and in
information technology, though not as commonly used in
scientific method, and in the philosophy of science. Indeed, we
had argued that the term needed to be commonized and
popularized in various fields of the science. In sum, the term
latency may be taken to mean the delay between a request for
information or action and a resultant response, or even in some
cases, the time it takes for data to travel from one point of a
system or a network to another. It is therefore, often seen as a
yardstick for performance or efficiency. Therefore, the time
taken for various steps or stages in a scientific process to be
completed, must be brought down, otherwise inefficiencies,
inordinate delays, or tardiness might result. 16
 Time Crashing Techniques
Fast-tracking and time crashing techniques are important
and widely utilized techniques in project management with a
view to accelerating project schedules, and intensifying
activities. These techniques, are however not commonly used
in scientific methods, but may have important implications for
research design' International Journal of Nursing Practice,
12(1): 8–13.
15
Charmaz, Kathy (2008) 'Constructionism and the grounded
theory method', in Holstein, J.A. and Gubrium, J.F. (eds.),
Handbook of Constructionist Research. New York: The
Guilford Press. pp. 397–412
16
Foundations of Data Intensive Applications Large Scale
Data Analytics Under the Hood. 2021

the field nonetheless. One technique in project management is
to add more resources. Often, CPM and critical path method is
used, along with PERT, or program evaluation and review
technique. These are often used for planning and managing
large and complex projects, and reducing latency time, reducing
cost, or enhancing efficiency. Sometimes fast tracking is also
used, and multiple activities are begun or complete in parallel.
There are often accompanied by the use of suitable metrics and
measurements, and suitable metrics must be devised for the
completion of research and research processes too. This is one
area needing urgent attention, and one contemporary
researchers must work towards. 17 18 19 20
 Example of Loose-Ended Hypotheses
There are many different examples of loose-ended
hypotheses, or open-ended hypotheses that may persist over
time, or sometimes even nearly into eternity. Ethnography-
driven economic planning as a concept was proposed by us in a
paper pertaining to anthropological economics, though its core
concepts have not yet been implemented in most cases. We also
then have the demand curve where demand rises as prices fall,
and vice versa. In case of supply, suppliers demand more as
prices rise, and vice versa, albeit with a time lag. We also have
the concept of elasticity of demand which varies on a case to
case basis. Some goods are more elastic, while some goods are
less elastic. We had also proposed the concept of cultural
elasticity of demand in our previous on anthropological
economics. We also have Giffen goods where demand rises as
prices rise, and vice versa. This is represented by the Giffen
paradox. We also have income elasticity of demand as opposed
to the price elasticity of demand, and have inferior goods,
luxury goods, and normal goods as well. Science might
progress faster, and better quality science may result if the
concepts of this paper are borne in mind. Since, researchers are
constantly searching for ideas, and since researchers are
constantly under pressure from universities and research bodies
to deliver, these may open up new avenues and new vistas for
research.
We have other concepts such as Hick’s compensating
variation and Slutsky’s model. We also have less useful
concepts such as the production possibility curve, production
possibility frontier, indifference curves, marginal rate of
substitution, and the marginal rate of technical substitution. We
also have the theory of diminishing marginal utility, and the
theory of marginal utility of money – some consider this to be
constant, though it may decline in different cases and scenarios.
All these need to be tested over time – across time and space-
and comprehensively and thoroughly. This will lead to more
17
Sonenshein, Scott (2017). Stretch: Unlock the Power of Less
and Achieve More Than You Ever Imagined. Harper Business
18
Rubin, Tzameret H.; Aas, Tor Helge; Stead, Andrew (1 July
2015). "Knowledge flow in Technological Business Incubators:
Evidence from Australia and Israel". Technovation. 41–42: 11–
24
meaningful and thorough progress in science. On the contrary
what do we find? We have a large number of hypotheses
generated over time, and most of them are left dangling in mid-
air. They continue to remain unvalidated for perpetuity and for
eternity, while more and more counter theories continue to be
generated. We also then have less than meaningful theories
such as the theory of self-fulfilling prophecy, and social convoy
theory. Another hypothesis is this: Do underdogs understand
social problems better? Also, how do sub-replacement fertility
dynamics work in relation to the economy, economic growth,
and the environment? Patterns can be identified through
repeated testing, and exceptions identified through the
sociological ninety-ten rules. Studies must be constantly
performed and reperformed as new data is uncovered, or made
available.
II. CONCLUSION
The objective of this paper was to emphasize the need for
minimizing the time taken between hypothesis formulation or
hypothesis generation, hypotheses testing and hypothesis
refinement, and the role it could come to play in accelerating
scientific progress. We suitably began this paper by defining the
term hypothesis, delineating the different steps involved in the
formulation of a hypothesis, evaluating the different types of
hypotheses, and also by distinguishing and differentiating
hypotheses, theories and laws. We also then visited the concept
of a grounded theory, and explored the types, uses, and
limitations of grounded theory. We also then revisited the
concept of latency periods, a concept that we had discussed and
debated previously, to show how time crashing techniques
could be suitably employed to bring down timeframes in the
entire research cycle. We also then provided and furnished a
large number of examples from various fields in the social
sciences to illustrate how many hypotheses remain untested for
long periods in time, or remain dangling in mid-air. We do then
hope, anticipate and expect that this will prove to be an
important paper in twenty-first century science, and will
encourage and embolden researchers to take up hypotheses
verification and hypothesis ratification studies in large numbers
in the not too distant future. This will naturally lead to faster
progress in many fields of scientific activity.
19
Gomez-Mejia, Luis R.; David B. Balkin; Robert L. Cardy
(2008). Management: People, Performance, Change (3 ed.).
New York
20
Jacoby, S.M. (1985). "Employing Bureaucracy: Managers,
Unions, and the Transformation of Work in American Industry,
1900-1945". Columbia University Press

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