The document discusses hypotheses, providing definitions and discussing the nature, types, and formulation of hypotheses. It defines a hypothesis as a tentative statement about the relationship between two or more variables that can be tested. The main types discussed are the null hypothesis, which represents a theory to be tested, and the alternative hypothesis, which is the opposite of the null hypothesis. It also discusses how hypotheses are formulated differently for qualitative versus quantitative research, with qualitative research often using research questions rather than hypotheses.
Research is the systematic efforts of gathering, analyzing & interpreting the problems confronted by humanity.
this ppt contains following points :-
Meaning of research
Characteristics of Research
Objectives of Research
Motivation in Research
Importance of Research
Types of Research
Research Process
Difference Between Research Methods & Research Methodology
Meaning of Business Research
Role of Business Research
Factors Affecting Business Research
Applied research - Research Methodology - Manu Melwin Joymanumelwin
Applied research is a form of systematic inquiry involving the practical application of science. It accesses and uses some part of the research communities' (the academia's) accumulated theories, knowledge, methods, and techniques, for a specific, often state-, business-, or client-driven purpose.
Exploratory Research Design - Meaning and MethodsSundar B N
This ppt contains Exploratory Research Design which covers Introduction to Exploratory Research, Meaning of Exploratory Research, Techniques of Exploratory Research, Examples of Exploratory Research, Methods of Designing Exploratory Research
Research, Types and objectives of research Bindu Kshtriya
This presentation is regarding the basics of research method, about the voyage of research, steps included in research, types of research including descriptive, analytical, applied, fundamental, quantitative, qualitative conceptual, empirical historical conclusion oriented etc
A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research with economy in procedure.
It is a conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement and analysis of data.
Types of Hypothesis-Advance Research MethodologyRehan Ehsan
This Presentation states the details of Hypothesis for students to get help in advance research methodology. Rearchers may also get help from this work.
Research is the systematic efforts of gathering, analyzing & interpreting the problems confronted by humanity.
this ppt contains following points :-
Meaning of research
Characteristics of Research
Objectives of Research
Motivation in Research
Importance of Research
Types of Research
Research Process
Difference Between Research Methods & Research Methodology
Meaning of Business Research
Role of Business Research
Factors Affecting Business Research
Applied research - Research Methodology - Manu Melwin Joymanumelwin
Applied research is a form of systematic inquiry involving the practical application of science. It accesses and uses some part of the research communities' (the academia's) accumulated theories, knowledge, methods, and techniques, for a specific, often state-, business-, or client-driven purpose.
Exploratory Research Design - Meaning and MethodsSundar B N
This ppt contains Exploratory Research Design which covers Introduction to Exploratory Research, Meaning of Exploratory Research, Techniques of Exploratory Research, Examples of Exploratory Research, Methods of Designing Exploratory Research
Research, Types and objectives of research Bindu Kshtriya
This presentation is regarding the basics of research method, about the voyage of research, steps included in research, types of research including descriptive, analytical, applied, fundamental, quantitative, qualitative conceptual, empirical historical conclusion oriented etc
A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research with economy in procedure.
It is a conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement and analysis of data.
Types of Hypothesis-Advance Research MethodologyRehan Ehsan
This Presentation states the details of Hypothesis for students to get help in advance research methodology. Rearchers may also get help from this work.
Writing introduction, hypothesis and objectives of a thesis and scientific pa...Md. Nazrul Islam
This is the guideline for writing a thesis or scientific paper for MS students.
- Introduction
- Background and Setting
- Identification of Problem
- Definitions of hypothesis
- Types of hypotheses
- Guidelines for writing objectives and research questions
- Purpose Statement
- Objectives or Research Questions
- Assumptions
- Limitations
- Significance of The Study
For a detailed explanation Watch the Youtube video:
https://youtu.be/6g4tD162yhI
Hypothesis, Characteristics of a good hypothesis, contribution to research study, Types of hypothesis, Source, level of significance, two-tailed one-tailed test, types of errors
The economic growth potential that can result from shift in a Population’s age structure, mainly when the share of working age population (15-64) is larger than the non-working age share of the population(14 Years and younger and 65 years and older)
working age population is the population in the age group of 15-64 in the economy currently employed.
People who are still undergoing studies, housewives and persons younger than 15 and above the age of 64 are not reckoned in the labour force. Labour Force Participation Rate (LFPR) is defined as the number of persons in the labour force divided by the total working age population.
The Planning Commission set up a Working Group in 1962. It recommended that the national minimum for a household of 5 persons should be not less than Rs. 100/- per month for rural and Rs. 125/- for urban at 1960-61 prices.
Environment means the surroundings or conditions of life, may be social, political, economic, cultural, natural etc.
Natural resources are used with other man made resources in order to produce goods in agriculture, industry or other spheres of economic activity.
Unit 4 c) changes in policy perspectives role of institutional framework afte...Mahendra Kumar Ghadoliya
Development of Indian economy has passed from many phases. We followed the policy of Import Substitution and restrictive trade policies. we liberalized the economy gradually and slowly. After 1991 Industrial policy India followed path of Liberalization.
Meaning of economic development, core values in economic development, Developed countries, Underdeveloped countries, Characteristics , Difference between Economic Growth and Economic Development.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. Introduction
Processes involved before formulating the
hypotheses.
Definition
Nature of Hypothesis
Types
How to formulate a Hypotheses in
Quantitative Research
Qualitative Research
Testing and Errors in Hypotheses
Summary
3. The research structure helps us create research that is :
•Quantifiable
•Verifiable
•Replicable
• Defensible
•Corollaries among the model, common sense & paper format
•MODEL COMMON SENSE PAPER
FORMAT
•Research Question Why Introduction
•Develop a Theory your Answer Methodology
•Identify variables How Analysis
•Identify hypothesis Expectations result
•Test Collect Analyze data conclusion
•Evaluate Result What it Means
•Critical Review What it doesn't Mean
4. Most research projects share the same general structure,
which could be represented in the shape of an
“Hourglass”.
The ‘Hour Glass’ notion of research allows someone to
breakdown what can see an overwhelming amount of
information into manageable chunks, and allows you to
see the whole picture in an understandable way.
BEGIN WITH BROAD QUESTIONS
NARROW DOWN, FOCUS IN
OPERATIONALIZE
OBSERVE
ANALYZE DATA
REACH CONCLUSIONS
GENERALIZE
GENERALIZE BACK TO QUESTIONS
5. Where does the problem origination or discovery
begin
Preview experience Triggered interest potential
problem field
Criteria of problem and problem statement
Goal and planning
Search Explore and Gather the Evidences
Generate creative and logical alternative
solutions.
6. Definition of Hypothesis:
“Hypotheses are single tentative guesses, good hunches –
assumed for use in devising theory or planning experiments
intended to be given a direct experimental test when possible”.
(Eric Rogers, 1966)
“A hypothesis is a conjectural statement of the relation
between two or more variables”. (Kerlinger, 1956)
“Hypothesis is a formal statement that presents the expected
relationship between an independent and dependent variable.”
(Creswell, 1994)
“A research question is essentially a hypothesis asked in the
form of a question.”
7. Definition of Hypothesis:
“It is a tentative prediction about the nature of the relationship
between two or more variables.”
“A hypothesis can be defined as a tentative explanation of the
research problem, a possible outcome of the research, or an
educated guess about the research outcome.” (Sarantakos, 1993:
1991)
“Hypotheses are always in declarative sentence form, and they
relate, either generally or specifically , variables to variables.”
“An hypothesis is a statement or explanation that is suggested
by knowledge or observation but has not, yet, been proved or
disproved.” (Macleod Clark J and Hockey L 1981)
8. Nature of Hypothesis:
The hypothesis is a clear statement of what is intended to be
investigated. It should be specified before research is conducted
and openly stated in reporting the results.
This allows to:
Identify the research objectives
Identify the key abstract concepts involved in the research
Identify its relationship to both the problem statement and
the literature review
A problem cannot be scientifically solved unless it is reduced to
hypothesis form
It is a powerful tool of advancement of knowledge, consistent
with existing knowledge and conducive to further enquiry
9. Nature of Hypothesis:
It can be tested – verifiable or falsifiable
Hypotheses are not moral or ethical questions
It is neither too specific nor to general
It is a prediction of consequences
It is considered valuable even if proven false
10. An Example…
Imagine the following situation:
You are a nutritionist working in a zoo, and one of your responsibilities is to develop a
menu
plan for the group of monkeys. In order to get all the vitamins they need, the monkeys have
to be given fresh leaves as part of their diet. Choices you consider include leaves of the
following species: (a) A (b) B (c) C (d) D and (e) E. You know that in the wild the monkeys
eat mainly B leaves, but you suspect that this could be because they are safe whilst feeding
in B trees, whereas eating any of the other species would make them vulnerable to
predation. You design an experiment to find out which type of leaf the monkeys actually
like
best: You offer the monkeys all five types of leaves in equal quantities, and observe what
they eat.
There are many different experimental hypotheses you could formulate for the monkey
study. For example:
When offered all five types of leaves, the monkeys will preferentially feed on B leaves.
This statement satisfies both criteria for experimental hypotheses. It is a
•Prediction: It predicts the anticipated outcome of the experiment
•Testable: Once you have collected and evaluated your data (i.e. observations of what the
monkeys eat when all five types of leaves are offered), you know whether or not they ate
more B leaves than the other types.
11. Incorrect hypotheses would include:
When offered all five types of leaves, the monkeys will preferentially eat the
type they like best.
This statement certainly sounds predictive, but it does not satisfy the second
criterion: there is no way you can test whether it is true once you have the
results of your study. Your data will show you whether the monkeys preferred
one type of leaf, but not why they preferred it (i.e., they like it best). I would, in
fact, regard the above statement as an assumption that is inherent in the design
of this experiment, rather than as a hypothesis.
When offered all five types of leaves, the monkeys will preferentially eat B
leaves because they can eat these safely in their natural habitat. This statement
is problematic because its second part ('because they can eat these safely in
their natural habitat') also fails to satisfy the criterion of testability. You can tell
whether the monkeys preferentially eat those leaves, but the results of this
experiment cannot tell you why. In their natural habitat, howler monkeys that
feed in B trees are less vulnerable to predation than monkeys that feed on A, C,
D, or E.
12. This is a perfectly good experimental hypothesis, but
not for the experiment described in the question. You
could use this hypothesis if you did a study in the wild
looking at how many monkeys get killed by predators
whilst feeding on the leaves of A, B etc. However, for the
experimental feeding study in the zoo it is neither a
prediction nor testable.
When offered all five types of leaves, which type will
the monkeys eat preferentially? This is a question, and
questions fail to satisfy criterion #1: They are not
predictive statements. Hence, a question is not a
hypothesis.
14. The null hypothesis represents a theory that has been
put forward, either because it is believed to be true or
because it is to be used as a basis for argument, but
has not been proved.
Has serious outcome if incorrect decision is made!
The alternative hypothesis is a statement of what a
hypothesis test is set up to establish.
Opposite of Null Hypothesis.
Only reached if H0 is rejected.
Frequently “alternative” is actual desired conclusion
of the researcher!
15. In a clinical trial of a new drug, the null hypothesis might be
that the new drug is no better, on average, than the current
drug.
We would write H0: there is no difference between
the two drugs on average.
The alternative hypothesis might be that: the new drug has a
different effect, on average, compared to that of the current
drug.
We would write H1: the two drugs have different
effects, on average. the new drug is better, on
average, than the current drug.
We would write H1: the new drug is better than the
current drug, on average.
16. We give special consideration to the null hypothesis…
This is due to the fact that the null hypothesis relates to the
statement being tested, whereas the alternative hypothesis
relates to the statement to be accepted if / when the null is
rejected.
The final conclusion, once the test has been carried out, is
always given in terms of the null hypothesis. We either 'reject
H0 in favour of H1' or 'do not reject H0'; we never conclude
'reject H1', or even 'accept H1 '.
If we conclude 'do not reject H0', this does not necessarily
mean that the null hypothesis is true, it only suggests that there
is not sufficient evidence against H0 in favour of H1; rejecting
the null hypothesis then, suggests that the alternative
hypothesis may be true.
17. Formulating a hypothesis
Formulating Hypothesis is important to narrow
a question down to one that can reasonably be
studied in a research project.
The formulation of the hypothesis basically
varies with the kind of research project
conducted:
QUALITATIVE
QUANTITATIVE
18. can be divided into;
Deductive
Inductive
Theory
Hypothesis
Observation
Confirmation
Pattern
Tentative Hypothesis
Theory
observation
19. Qualitative Approach
The use of Research Questions as opposed to
objectives or hypothesis, is more frequent.
Characteristics
Use of words- what or how.
Specify whether the study: discovers, seeks to
understand, explores or describes the experiences.
Use of non-directional wording in the question.
These questions describe, rather than relate variables
or compare groups during study.
The questions are usually open-ended, without
reference to the literature or theory.
Use of a single focus.
20. The rules of Qualitative research
Kleining offers four rules for a scientific and
qualitative process of approaching
understanding to reality.
Rule 1 (refers to subject / researcher)
"Prior understandings of the phenomenon to
be researched should be seen as provisional
and should be transcended with [the discovery
of] new information with which they are not
consistent."
21. The rules of Qualitative research
Rule 2 (refers to the object of study)
"The object is provisional; it is only fully known after the
successful completion of the process of discovery."
Rule 3 (refers to action in relation to the subject of
research, hence to data collection)
"The object should be approached from "all" sides; rule of
the maximum variation of perspectives.“
Rule 4 (refers to the evaluation of information gathered,
hence to data analysis)
"Analysis of the data for common elements."
22. Quantitative Approach
In survey projects the use of research questions and
objectives is more Frequent.
In experiments the use of hypotheses are more
frequent.
comparison between variables
Represent,
relationship between variables
Characteristics :
The testable proposition to be deduced from theory.
Independent and dependent variables to be separated
and measured separately.
23. Characteristics contd...
Independent and dependent variables to
be separated and measured separately.
To be either writing-questions, or
objectives or hypotheses, but not a
combination.
Consider the alternative forms for writing
and make a choice based on the audience
for the research
24. Generation of Research
Hypothesis
Problem statements become research
hypotheses when constructs are operationalized
Initial Ideas
(often vague and general)
Initial observations Search of existing research
literature
Statement of the problem
Operational definitions of
constructs
Research hypothesis
(a specific deductive prediction)
25. Quantitative Approach
To be either writing-questions, or
objectives or hypotheses, but not a
combination.
Consider the alternative forms for writing
and make a choice based on the audience
for the research
26. Example:
Consider the example of a simple association
between two variables, Y and X.
1.Y and X are associated (or, there is an
association between Y and X).
2. Y is related to X (or, Y is dependent
on X).
3. As X increases, Y decreases (or,
increases in values of X appear to effect
reduction in values of Y).
27. Contd…
The first hypothesis provides a simple statement of association
between Y and X. Nothing is indicated about the association that
would allow the researcher to determine which variable, Y or X,
would tend to cause the other variable to change in value.
The second hypothesis is also a simple statement of association
between Y and X, but this time it may be inferred that values of Y
are in some way contingent upon the condition of the X variable.
The third hypothesis is the most specific of the three. Not only
does it say that Y and X are related and that Y is dependent on X for
its value, but it also reveals something more about the nature of the
association between the two variables.
28. Testing and Chhalenge-
The degree of challenge to the hypothesis will
depend on the type of problem and its importance. It
can range from just seeking “a good enough” solution
to a much more rigorous challenge.
The term Challenge may include
Verification Justification Refutability
Validity Rectification Repeatability
Falsification
There are two possibilities
1. Nothing Happened the Null Hypothesis - Ho
2. Something Happened the Alternative Hypothesis - H1
29. Hypothesis testing is a four step procedure:
1.Stating the hypothesis (Null or
Alternative)
2. Setting the criteria for a decision
3. Collecting data
4. Evaluate the Null hypothesis
30. Hypothesis testing is a four step procedure:
Hypothesis testing involves making a decision
concerning some hypothesis or statement about a
population parameter such as the population mean,
using the sample mean, to decide whether this
statement about the value of is valid or not.
The steps of the hypothesis testing :
1- The first step is to formulate a null hypothesis
written . The statement for is usually expressed
as an equation or inequality as follows:
H0: µ = given Value, H0: µ ≤ given Value,
H0: µ ≥given Value
31. Hypothesis testing is a four step procedure:
The alternative hypothesis suggests the direction of
the actual value of the parameter relative to the
stated value. The statement of in the form of an
inequality that indicates that the investigator has no
opinion as to whether the actual value of is more
than or less than the stated value but the feeling is
that the stated value is incorrect. In this case the test
is two-tail test. Statements in the form of strictly
greater than or strictly less than relationship indicate
that the investigator has an opinion as to the
direction of the value of the parameter relative to the
stated value. In this case it is called one-tail test.
32. Hypothesis testing is a four step procedure:
State the level of significance of the test and the
corresponding Z values (for large sample tests), or the
corresponding T values ( for small sample tests). The
hypothesis test is frequently conducted at the 5%, 1% and 10%
levels of significance. Some can use the Z values. For a test
conducted at any other level of significance, we simply use the
normal distribution table to determine a corresponding Z
value.
3- Calculate the test statistic for the sample that has taken.
4- Determine the boundary (or boundaries) for the area of
rejection regions using either or values. A critical value is
the boundary or limit value that requires as to reject the
statement of the null hypothesis.
33. Hypothesis testing is a four step procedure:
Also in this step it is stated an alternative hypothesis,
written , a statement that indicates the opinion of
the conductor of the test as to the actual value of .
is expressed as follows:
Ha: µ ≠given Value, Ha: µ >given Value,
Ha: µ <given Value
We conduct a hypothesis test on a given value to find
out if actual observation would lead us to reject the
stated value.
34. Two types of mistakes are possible while testing a
Hypothesis :
Type I
Type II
Example:
Your actual health
doctor Sick well
Says Sick You are sick Doctor confirms it Get scared nothing
RIGHT wrong Type I error
Well Doctor missed your real sickness you are really not sick
Type II error RIGHT
35. Type I Error:
A type I error occurs when the null hypothesis
(H0) is wrongly rejected.
For example: A type I error would occur if we
concluded that the two drugs produced
different effects when in fact there was no
difference between them.
36. Type II Error:
A type II error occurs when the null hypothesis
H0, is not rejected when it is in fact false.
For example: A type II error would occur if it
were concluded that the two drugs produced
the same effect, that is, there is no difference
between the two drugs on average, when in
fact they produced different ones..
37. Reject H0 Don’t Reject Ho
H0 Type I Error Right Decision
H1 Right Decision Type II Error
To Generalise:
A type I error is often considered to be more serious, and therefore more important to
avoid, than a type II error.
T
R
U
T
H
38. Research questions and hypotheses become
“signposts” for explaining the purpose of the
study & guiding the research…”, Creswell
A hypothesis is an explanation, tentative
and unsure of itself, for specific phenomena
about which you have questions.
A well-crafted hypothesis very often suggests
the best way to perform the research and
gives you clues as to your research design
39. There are different types of hypotheses.
Deductive
Inductive
Research Hypothesis can either be non-directional or
directional. There exists a hypothesis that is opposite
of the positively stated one, i.e. the null hypothesis
Thus to conclude it would be fitting to say “hypothesis
is perhaps the most powerful tool, man has invented
to achieve dependable knowledge” – Fred Kerlinger…