Business Research Methods Unit 2 notes

Notes by Prof. SUJEET TAMBE
Notes by Prof. SUJEET TAMBE Page 1
BUSINESS RESEARCH METHODS
UNIT II
2.1 RESEARCH DESIGN
Concept
The research design refers to the overall strategy that you choose to integrate the different
components of the study in a coherent and logical way, thereby, ensuring you will effectively
address the research problem; it constitutes the blueprint for the collection, measurement, and
analysis of data. Note that your research problem determines the type of design you should use,
not the other way around!
The function of a research design is to ensure that the evidence obtained enables you to
effectively address the research problem logically and as unambiguously as possible. In social
sciences research, obtaining information relevant to the research problem generally entails
specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately
describe and assess meaning related to an observable phenomenon.
Definition
According to Kerlinger
Research design is the plan, structure and strategy of investigation conceived so as to obtain
answers to research questions and to control variance.
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 purpose with economy in procedure.
Features of a Good Research Design
In the view of various definition of research design, the following characteristics are found.
1. A good research design is an ethical research design;
2. A good research design is one that is capable of obtaining the most reliable and valid data;
3. A good research design is one that is capable of measuring any odd events in any
circumstances;

4. A good research design is one that helps an investigator avoid making mistaken conclusions;
5. A good research design is one that can adequately control the various threats of validity, both
internal and external.
Need of Research Design
Research design is needed because it facilitates the smooth sailing of the various research
operations, thereby making research as efficient as possible yielding maximal information with
minimal expenditure of effort, time and money.
Research design stands for advance planning of the method to be adopted for collecting the
relevant data and the techniques to be used in their analysis, keeping in view the objective of the
research and the availability of staff, time and money.
Use of good research design
1. Consumes less time.
2. Ensures project time schedule.
3. Helps researcher to prepare himself to carry out research in a proper and a systematic way.
4. Better documentation of the various activities while the project work is going on.
5. Helps in proper planning of the resources and their procurement in right time.
6. Provides satisfaction and confidence, accompanied with a sense of success from the beginning
of the work of the research project.

2.2 QUALITATIVE & QUANTITATIVE RESEARCH
Comparison of qualitative & quantitative research
Qualitative Quantitative
Definitions a systematic subjective
approach used to describe life
experiences and give them
meaning
a formal, objective, systematic
process for obtaining
information about the world. A
method used to describe, test
relationships, and examine cause
and effect relationships.
Goals To gain insight; explore the
depth, richness, and
complexity inherent in the
phenomenon.
To test relationships, describe,
examine cause and effect
relations
Characteristics  Soft science
 Focus: complex &
broad
 Holistic
 Subjective
 Dialectic, inductive
reasoning
 Basis of knowing:
meaning & discovery
 Develops theory
 Shared interpretation
 Communication &
observation
 Basic element of
analysis: words
 Individual interpretation
 Uniqueness
 Hard science
 Focus: concise & narrow
 Reductionistic
 Objective
 Logistic, deductive
reasoning
 Basis of knowing: cause &
effect, relationships
 Tests theory
 Control
 Instruments
 Basic element of analysis:
numbers
 Statistical analysis
 Generalization

Qualitative Research
Pros of qualitative research
 Rich, in-depth detail is possible (e.g. participants can elaborate on what they mean)
 Perceptions of participants themselves can be considered (the human factor)
 Appropriate for situations in which detailed understanding is required
 Events can be seen in their proper context / more holistically
Cons of qualitative research
 Not always generalizable due to small sample sizes and the subjective nature of the
research
 Conclusions need to be carefully hedged
 Accusations of unreliability are common (different results may be achieved on a different
day/with different people)
Quantitative Research
Pros of quantitative research
 Larger sample sizes often make the conclusions from quantitative research generalizable
 Statistical methods mean that the analysis is often considered reliable
 Appropriate for situations where systematic, standardised comparisons are needed
Cons of quantitative research
 Does not always shed light on the full complexity of human experience or perceptions
 Can reveal what / to what extent, but cannot always explore why or how
 May give a false impression of homogeneity in a sample

2.3 Exploratory Research Design
Exploratory Means to Explore the hidden things, which are not clearly visible. Exploratory
research is a type of Research conducted for a problem that has not been clearly defined.
Exploratory Research Studies are also termed as formulate Research studies.
For Example,
It is one thing to describe the crime rate in a country, to examine trends over time or to compare
the rates in different countries, it is quite different thing to develop explanations about why the
crime rate is as high as it is why some types of crime are increasing or why the rate is higher in
some countries than in others.
Exploratory Research provides insights into and comprehension of an issue or situation. It
draws definitive conclusions only with extreme caution. Exploratory research is a type of
research conducted because a problem has not been clearly defined.
Exploratory research helps determine the best research design, data collection method and
selection of subjects. Given its fundamental nature, Exploratory Research often concludes that a
perceived problem does not actually exist.
1) Experience Survey
In experience surveys, it is desirable to talk to persons who are well informed in the area being
investigated. These people may be company executives or persons outside the organisation.
Here, no questionnaire is required. The approach adopted in an experience survey should be
highly unstructured, so that the respondent can give divergent views. Since the idea of using
experience survey is to undertake problem formulation, and not conclusion, probability sample
need not be used. Those who cannot speak freely should be excluded from the sample.
Examples :
1) A group of housewives may be approached for their choice for a “ready to cook product”.
2) A publisher might want to find out the reason for poor circulation of newspaper introduced
recently. He might meet (a) Newspaper sellers (b) Public reading room (c) General public (d)
Business community; etc.
These are experienced persons whose knowledge researcher can use.

2) Focus Group
Another widely used technique in exploratory research is the focus group. In a focus group, a
small number of individuals are brought together to study and talk about some topic of interest.
The discussion is co-ordinated by a moderator. The group usually is of 8-12 persons. While
selecting these persons, care has to be taken to see that they should have a common background
and have similar experiences in buying. This is required because there should not be a conflict
among the group members on the common issues that are being discussed. During the
discussion, future buying attitudes, present buying opinion etc., are gathered.
Most of the companies conducting the focus groups, first screen the candidates to determine
who will compose the particular group. Firms also take care to avoid groups, in which some of
the participants have their friends and relatives, because this leads to a biased discussion.
Normally, a number of such groups are constituted and the final conclusion of various groups
are taken for formulating the hypothesis. Therefore, a key factor in focus group is to have
similar groups. Normally there are 4-5 groups. Some of them may even have 6-8 groups. The
guiding criteria is to see whether the latter groups are generating additional ideas or repeating
the same with respect to the subject under study. When this shows a diminishing return from the
group, the discussions stopped. The typical focus group lasts for 1-30 hours to 2 hours. The
moderator under the focus group has a key role. His job is to guide the group to proceed in the
right direction.
3) Projective Techniques
They are indirect and unstructured methods of investigation which have been developed by the
psychologists and use projection of respondents for inferring about underline motives, urges or
intentions which cannot be secure through direct questioning as the respondent either resists to
reveal them or is unable to figure out himself. These techniques are useful in giving respondents
opportunities to express their attitudes without personal embarrassment. These techniques helps
the respondents to project his own attitude and feelings unconsciously on the subject under
study. Thus Projective Techniques play a important role in motivational researches or in attitude
surveys.
Important Projective Techniques
1. Word Association Test: An individual is given a clue or hint and asked to respond to the
first thing that comes to mind. The association can take the shape of a picture or a word.
There can be many interpretations of the same thing. A list of words is given and you
don’t know in which word they are most interested. The interviewer records the
responses which reveal the inner feeling of the respondents. The frequency with which
any word is given a response and the amount of time that elapses before the response is

given are important for the researcher. For eg: Out of 50 respondents 20 people associate
the word “ Fair” with “Complexion”.
2. Completion Test: In this the respondents are asked to complete an incomplete sentence
or story. The completion will reflect their attitude and state of mind.
3. Construction Test: This is more or less like completion test. They can give you a picture
and you are asked to write a story about it. The initial structure is limited and not detailed
like the completion test. For eg: 2 cartoons are given and a dialogue is to written.
4. Expression Techniques: In this the people are asked to express the feeling or attitude of
other people.
Disadvantages of Projective Techniques
1. Highly trained interviewers and skilled interpreters are needed.
2. Interpreters bias can be there.
3. It is a costly method.
4. The respondent selected may not be representative of the entire population.
4) Depth Interview
They generally use small samples and also conduct direct one to one personal interviews. A
detailed background is provided by the respondents and elaborate data concerning the
respondents opinions, values, motivation, expression, feeling etc are obtained. Even their non-
verbal expressions are observed. They take long time, therefore lengthy observations are
involved.
These are conducted to customize individual responses. The questions will depend on what kind
of answers are given. Even interview climate influences the respondents. The success of
interviews depends on the rapport of the interviewers established with the respondents.
Advantages of Depth Interview
1. Lot of detail is provided.
2. Information obtained is comparatively more accurate.
3. Personal or intimate topic can also be discussed since the personal rapport is established
between the respondent and the interviewer
Disadvantages of Depth Interview
1. It is difficult to generalize since the interviewers are non-standardized
2. Since the success depends on the interviewer, there are chances of bias.
3. Data analysis takes a lot of time.

5) Observation Method
The observation method involves human or mechanical observation of what people actually do
or what events take place during a buying or consumption situation. “Information is collected
by observing process at work. ”The following are a few situations:-
1. Service Stations-Pose as a customer, go to a service station and observe.
2. To evaluate the effectiveness of display of Dunlop Pillow Cushions-In a departmental
store, observer notes:- a) How many pass by; b) How many stopped to look at the
display; c) How many decide to buy.
3. Super Market-Which is the best location in the shelf? Hidden cameras are used.
4. To determine typical sales arrangement and find out sales enthusiasm shown by various
salesmen-Normally this is done by an investigator using a concealed tape-recorder.
Advantages of Observation Method
1. If the researcher observes and record events, it is not necessary to rely on the willingness
and ability of respondents to report accurately.
2. The biasing effect of interviewers is either eliminated or reduced. Data collected by
observation are, thus, more objective and generally more accurate.
Disadvantages of Observation Method
1. The most limiting factor in the use of observation method is the inability to observe such
things such as attitudes, motivations, customers/consumers state of mind, their buying
motives and their images.
2. It also takes time for the investigator to wait for a particular action to take place.
3. Personal and intimate activities, such as watching television late at night, are more easily
discussed with questionnaires than they are observed.
4. Cost is the final disadvantage of observation method. Under most circumstances,
observational data are more expensive to obtain than other survey data. The observer has
to wait doing nothing, between events to be observed. The unproductive time is an
increased cost.

2.4 DESCRIPTIVE RESEARCH DESIGN
Sometimes an individual wants to know something about a group of people. Maybe the
individual is a would-be senator and wants to know who they're representing or a surveyor who
is looking to see if there is a need for a mental health program.
Descriptive research is a study designed to depict the participants in an accurate way. More
simply put, descriptive research is all about describing people who take part in the study.
There are three ways a researcher can go about doing a descriptive research project, and they
are:
 Observational, defined as a method of viewing and recording the participants
 Case study, defined as an in-depth study of an individual or group of individuals
 Survey, defined as a brief interview or discussion with an individual about a specific
topic
I. Cross Sectional Research Design
In medical research and social science, a cross-sectional study (also known as across-
sectional analysis, transverse study, prevalence study) is a type of observational study that
analyzes data from a population, or a representative subset, at a specific point in time—that
is, cross-sectional data.
A cross sectional study, on the other hand, takes a snapshot of a population at a certain time,
allowing conclusions about phenomena across a wide population to be drawn. An example of
a cross-sectional study would be a medical study looking at the prevalence of breast cancer in
a population.
Advantages of Cross-Sectional Study
The advantages of cross-sectional study include:
 Used to prove and/or disprove assumptions
 Not costly to perform and does not require a lot of time
 Captures a specific point in time
 Contains multiple variables at the time of the data snapshot
 The data can be used for various types of research
 Many findings and outcomes can be analyzed to create new theories/studies or in-depth
research

Disadvantages of Cross-Sectional Study
The disadvantages of cross-sectional study include:
 Cannot be used to analyze behavior over a period to time
 Does not help determine cause and effect
 The timing of the snapshot is not guaranteed to be representative
 Findings can be flawed or skewed if there is a conflict of interest with the funding source
II. LONGITUDINAL RESEARCH DESIGN
A longitudinal study is an observational research method in which data is gathered for the
same subjects repeatedly over a period of time. Longitudinal research projects can extend over
years or even decades. In a longitudinal cohortstudy, the same individuals are observed over
the study period.
Advantages of Longitudinal Studies
 They are effective in determining variable patterns over time. ...
 They can ensure clear focus and validity. ...
 They are very effective in doing research on developmental trends. ...
 They are more powerful than cross-sectional studies. ...
 They are highly flexible.

2.5 EXPERIMENTAL RESEARCH DESIGN
Experimental research is any research conducted with a scientific approach, where a set of
variables are kept constant while the other set of variables are being measured as the subject of
experiment.
Experimental research is one of the founding quantitative research methods.
The simplest example of an experimental research is conducting a laboratory test. As long as
research is being conducted under scientifically acceptable conditions – it qualifies as an
experimental research. A true experimental research is considered to be successful only when
the researcher confirms that a change in the dependent variable is solely due to the manipulation
of the independent variable.
It is important for an experimental research to establish cause and effect of a phenomenon,
which means, it should be definite that effects observed from an experiment are due to the
cause. As naturally, occurring event can be confusing for researchers to establish conclusions.
For instance, if a cardiology student conducts research to understand the effect of food on
cholesterol and derives that most heart patients are non-vegetarians or have diabetes. They are
aspects (causes) which can result in a heart attack (effect).
Experimental research is conducted in the following situations:
Time is a vital factor for establishing a relationship between cause and effect.
Invariable behavior between cause and effect.
The eminence of cause-effect relationship is as per desirability.
What is Causal Research, and Why is it Important?
Causal research falls under the category of conclusive research, because of its attempt to reveal
a cause and effect relationship between two variables. Like descriptive research, this form of
research attempts to prove an idea put forward by an individual or organization. However, it
significantly differs on both its methods and its purpose. Where descriptive research is broad in
scope, attempting to better define any opinion, attitude, or behaviour held by a particular group,
causal research will have only two objectives:

1. Understanding which variables are the cause, and which variables are the effect. For
example, let’s say a city council wanted to reduce car accidents on their streets. They
might find through preliminary descriptive and exploratory research that both accidents
and road rage have been steadily increasing over the past 5 years. Instead of
automatically assuming that road rage is the cause of these accidents, it would be
important to measure whether the opposite could be true. Maybe road rage increases in
light of more accidents due to lane closures and increased traffic. It could also be the case
of the old adage ‘correlation does not guarantee causation.’ Maybe both are increasing
due to another reason like construction, lack of proper traffic controls, or an influx of new
drivers.
2. Determining the nature of the relationship between the causal variables and the
effect predicted. Continuing with our example, let’s say the city council proved that road
rage had an increasing effect on the number of car accidents in the area. The causal
research could be used for two things. First measuring the significance of the effect, like
quantifying the percentage increase in accidents that can be contributed by road rage.
Second, observing how the relationship between the variables works (ie: enraged drivers
are prone to accelerating dangerously or taking more risks, resulting in more accidents).
These objectives are what makes causal research more scientific than its exploratory and
descriptive counter parts. In order to meet these objectives, causal researchers have to isolate
the particular variable they believe is responsible for something taking place, and measure its
true significance. With this information, an organization can confidently decide whether it is
worth the resources to use a variable, like adding better traffic signs, or attempt to eliminate a
variable, like road rage.
Implementing Causal Research Effectively
Causal research should be looked at as experimental research. Remember, the goal of this
research is to prove a cause and effect relationship. With this in mind, it becomes very
important to have strictly planned parameters and objectives. Without a complete understanding
of your research plan and what you are trying to prove, your findings can become unreliable and
have high amounts of researcher bias. Try using exploratory research or descriptive research as
a tool to base your research plan on.
Once your research plan and objectives are fleshed out, it’s time to set up your causal
experiment properly. Here are three major conditions about your causal experiment you’ll want
to check off before you set it into motion:
1. The cause and effect relationship will be proved or disproved by the experiment. Of
course this may seem like a no-brainer, but if you do not make sure your research plan
directly ties into your research objective, the end results of your study will be as fruitless

as most children’s cereals (no offense Tucan Sam). To make sure your study will have
results one way or another, observe what your normal environment is and then crank up
the frequency or power of the causal variable.
2. You are clearly identifying which variables are being tested as independent (causing
effect) and which are being tested as dependent (being effected). As discussed in the
road rage/car accident example, in many cases it is hard to tell which variable is
dependent on the other. Because of this, it is essential to identify which will be tested as
which prior to the experiment. Usually, the independent variable will be whatever you are
adding to the environment.
For example, we hypothesize that increasing colour options for cars will increase sales. In
this case, the number of colour options is the independent variable and the level of sales
is our dependent variable. Your next step would be to measure the normal rate of sales at
the car store, and then add a broader selection of car colours. After collecting the new
sales numbers, compare the two data sets and study the effect on sales.
3. There are no external variables that can also be causing changes in your
results.Without accounting for all possible factors that might effect changes in your
dependent variable, you can’t be certain it is the variable being tested that is truly
responsible for causing the effects we measure. In the laboratory, scientists have the
luxury of being able to create a completely neutral environment. Unfortunately for the
rest of us, we have to deal with the environment we are given. So the most important
thing to do when creating your research plan is to ensure that your experiment occurs
under the most similar possible conditions as when you measured your normal results.
For example, let’s say you are an ice cream store owner and want to study the effect a
clown handing out balloons in front of your store will have on sales. Awesome idea, I
know! It would be a bad idea to use your summertime sales as your normal data source
and run your experiment in winter. Not only would that be cold for the clown, the
weather would have a huge effect on ice cream sales.
Who Uses Causal Research and How Can I Incorporate it in my Business Goals?
It really doesn’t matter what type of organization you are or what goals you have, causal
research can be used to benefit you. The goal of causal research is to give proof that a particular
relationship exists. From a company standpoint, if you want to verify that a strategy will work
or be confident when identifying sources of an issue, causal research is the way to go. Let’s take
a look at a few examples of how causal research could be implemented with different goals in
mind:

1. Increasing Customer Retention: Most franchise chains conduct causal research
experiments within their stores. In one case, a large auto-repair shop recently conducted
an experiment where select shops enforced a policy that an employee would have a one-
on-one with the client while their vehicle is being assessed. They were instructed to go
over any concerns and speak in layman’s terms about anything wrong with the car,
focusing on the client understanding the issues.
This experiment was implemented because of an online survey that identified a lack of
employee-client communication as being a barrier to repeat customers. After identifying
two solutions to this issue (facilitating discussion and increasing client understanding),
the company used this experiment to learn just how effective these solutions would be in
increasing customer retention. By comparing the sales in unchanged shops to those that
were part of the experiment, the company noticed a significant increase in customer
loyalty.
2. Community Initiatives: City councils often use causal research to measure the success
of their community initiatives. Let’s say the City of Ottawa conducted a survey and
learned that Ottawan’s were dissatisfied with current public transit options. They could
then set in motion a strategy to create more ‘Park and Rides’ to help more people be able
to ride the bus. After implementing this strategy they can resend the same survey and
measure what type of effect it has had on the overall satisfaction of public transit.
3. Effective Advertising: Advertising is one of the most common sectors for causal
research. Most times companies will test ad campaigns in small areas before expanding it
across all locations. The idea is to measure whether there is a sufficient increase in sales,
leads or public interest in those regions with the advertisement before committing fully.
Many organizations will take this experiment a step further by creating a survey asking
customers what made them visit or interested in their services. Now the business can
compare responses from customers in the experiment area to the responses of their
overall client base and see if there increase in traffic is a direct result of their advertising.

VARIABLES
Very simply, a VARIABLE is a measurable characteristic that varies. It may change from group
to group, person to person, or even within one person over time. There are six common variable
types:
DEPENDENT VARIABLES
Those show the effect of manipulating or introducing the independent variables. For
example, if the independent variable is the use or non-use of a new language teaching
procedure, then the dependent variable might be students' scores on a test of the
content taught using that procedure. In other words, the variation in the dependent
variable depends on the variation in the independent variable.
INDEPENDENT VARIABLES
Those are those that the researcher has control over. This "control" may involve
manipulating existing variables (e.g., modifying existing methods of instruction) or
introducing new variables (e.g., adopting a totally new method for some sections of a
class) in the research setting. Whatever the case may be, the researcher expects that
the independent variable(s) will have some effect on (or relationship with) the
dependent variables.
INTERVENING VARIABLES
Those refer to abstract processes that are not directly observable but that link the
independent and dependent variables. In language learning and teaching, they are
usually inside the subjects' heads, including various language learning processes
which the researcher cannot observe. For example, if the use of a particular teaching
technique is the independent variable and mastery of the objectives is the dependent
variable, then the language learning processes used by the subjects are the intervening
variables.
MODERATOR VARIABLES
Those affect the relationship between the independent and dependent variables by
modifying the effect of the intervening variable(s). Unlike extraneous variables,

moderator variables are measured and taken into consideration. Typical moderator
variables in TESL and language acquisition research (when they are not the major
focus of the study) include the sex, age, culture, or language proficiency of the
subjects.
CONTROL VARIABLES
Language learning and teaching are very complex processes. It is not possible to
consider every variable in a single study. Therefore, the variables that are not
measured in a particular study must be held constant, neutralized/balanced, or
eliminated, so they will not have a biasing effect on the other variables. Variables that
have been controlled in this way are called control variables.
EXTRANEOUS VARIABLES
Those are those factors in the research environment which may have an effect on the
dependent variable(s) but which are not controlled. Extraneous variables are
dangerous. They may damage a study's validity, making it impossible to know
whether the effects were caused by the independent and moderator variables or some
extraneous factor. If they cannot be controlled, extraneous variables must at least be
taken into consideration when interpreting results.

2.6 HYPOTHESIS
A hypothesis is an educated prediction that can be tested. You will discover the purpose of a
hypothesis then learn how one is developed and written. Examples are provided to aid your
understanding, and there is a quiz to test your knowledge.
What Is a Hypothesis?
Imagine you have a test at school tomorrow. You stay out late and see a movie with friends.
You know that when you study the night before, you get good grades. What do you think will
happen on tomorrow's test?
When you answered this question, you formed a hypothesis. A hypothesis is a specific, testable
prediction. It describes in concrete terms what you expect will happen in a certain circumstance.
Your hypothesis may have been, 'If not studying lowers test performance and I do not study,
then I will get a low grade on the test.'
Example
"If _____[I do this] _____, then _____[this]_____ will happen."
Sound familiar? It should. This formulaic approach to making a statement about what you
"think" will happen is the basis of most science fair projects and much scientific exploration.
The Purpose of a Hypothesis
A hypothesis is used in an experiment to define the relationship between two variables. The
purpose of a hypothesis is to find the answer to a question. A formalized hypothesis will force
us to think about what results we should look for in an experiment.
The first variable is called the independent variable. This is the part of the experiment that can
be changed and tested. The independent variable happens first and can be considered the cause
of any changes in the outcome. The outcome is called the dependent variable. The
independent variable in our previous example is not studying for a test. The dependent variable
that you are using to measure outcome is your test score.
Let's use the previous example again to illustrate these ideas. The hypothesis is testable because
you will receive a score on your test performance. It is measurable because you can compare
test scores received from when you did study and test scores received from when you did not
study.

A hypothesis should always:
 Explain what you expect to happen
 Be clear and understandable
 Be testable
 Be measurable
 And contain an independent and dependent variable
Qualities of a Good Hypothesis
 Power of Prediction
One of the valuable attribute of a good hypothesis is to predict for future. It not only clears the
present problematic situation but also predict for the future that what would be happened in the
coming time. So, hypothesis is a best guide of research activity due to power of prediction.
 Closest to observable things
A hypothesis must have close contact with observable things. It does not believe on air castles
but it is based on observation. Those things and objects which we cannot observe, for that
hypothesis cannot be formulated. The verification of a hypothesis is based on observable things.
 Simplicity
A hypothesis should be so dabble to every layman, P.V young says, “A hypothesis wo0uld be
simple, if a researcher has more in sight towards the problem”. W-ocean stated that, “A
hypothesis should be as sharp as razor’s blade”. So, a good hypothesis must be simple and have
no complexity.
 Clarity
A hypothesis must be conceptually clear. It should be clear from ambiguous information’s. The
terminology used in it must be clear and acceptable to everyone.
 Testability
A good hypothesis should be tested empirically. It should be stated and formulated after
verification and deep observation. Thus testability is the primary feature of a good hypothesis.
 Relevant to Problem

If a hypothesis is relevant to a particular problem, it would be considered as good one. A
hypothesis is guidance for the identification and solution of the problem, so it must be
accordance to the problem.
 Specific
It should be formulated for a particular and specific problem. It should not include
generalization. If generalization exists, then a hypothesis cannot reach to the correct
conclusions.
 Relevant to available Techniques
Hypothesis must be relevant to the techniques which is available for testing. A researcher must
know about the workable techniques before formulating a hypothesis.
 Fruitful for new Discoveries
It should be able to provide new suggestions and ways of knowledge. It must create new
discoveries of knowledge J.S. Mill, one of the eminent researcher says that “Hypothesis is the
best source of new knowledge it creates new ways of discoveries”.
 Consistency & Harmony
Internal harmony and consistency is a major characteristic of good hypothesis. It should be out
of contradictions and conflicts. There must be a close relationship between variables which one
is dependent on other.
Following tips for thinking about and writing good hypotheses.
 The question comes first. Before you make a hypothesis, you have to clearly identify the
question you are interested in studying.
 A hypothesis is a statement, not a question. Your hypothesis is not the scientific question
in your project. The hypothesis is an educated, testable prediction about what will
happen.
 Make it clear. A good hypothesis is written in clear and simple language. Reading your
hypothesis should tell a teacher or judge exactly what you thought was going to happen
when you started your project.
 Keep the variables in mind. A good hypothesis defines the variables in easy-to-measure
terms, like who the participants are, what changes during the testing, and what the effect
of the changes will be.

 Make sure your hypothesis is "testable." To prove or disprove your hypothesis, you need
to be able to do an experiment and take measurements or make observations to see how
two things (your variables) are related. You should also be able to repeat your experiment
over and over again, if necessary.
To create a "testable" hypothesis make sure you have done all of these things:
o Thought about what experiments you will need to carry out to do the test.
o Identified the variables in the project.
o Included the independent and dependent variables in the hypothesis statement.
(This helps ensure that your statement is specific enough.
 Do your research. You may find many studies similar to yours have already been
conducted. What you learn from available research and data can help you shape your
project and hypothesis.
 Don't bite off more than you can chew! Answering some scientific questions can involve
more than one experiment, each with its own hypothesis. Make sure your hypothesis is
a specific statement relating to a single experiment
What Is a Null Hypothesis?
A hypothesis is a speculation or theory based on insufficient evidence that lends itself to further
testing and experimentation. With further testing, a hypothesis can usually be proven true or
false. Let's look at an example. Little Susie speculates, or hypothesizes, that the flowers she
waters with club soda will grow faster than flowers she waters with plain water. She waters
each plant daily for a month (experiment) and proves her hypothesis true!
A null hypothesis is a hypothesis that says there is no statistical significance between the two
variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove. In the
example, Susie's null hypothesis would be something like this: There is no statistically
significant relationship between the type of water I feed the flowers and growth of the flowers.
A researcher is challenged by the null hypothesis and usually wants to disprove it, to
demonstrate that there is a statistically-significant relationship between the two variables in the
hypothesis.
What Is an Alternative Hypothesis?
An alternative hypothesis simply is the inverse, or opposite, of the null hypothesis. So, if we
continue with the above example, the alternative hypothesis would be that there IS indeed a
statistically-significant relationship between what type of water the flower plant is fed and
growth. More specifically, here would be the null and alternative hypotheses for Susie's study:

Null: If one plant is fed club soda for one month and another plant is fed plain water, there will
be no difference in growth between the two plants.
Alternative: If one plant is fed club soda for one month and another plant is fed plain water, the
plant that is fed club soda will grow better than the plant that is fed plain water.
What is level of significance?
The null hypothesis is rejected if the p-value is less than a predetermined level, α. α is called the
significance level, and is the probability of rejecting the null hypothesis given that it is true (a
type I error). It is usually set at or below 5%.
What is 5% level of significance?
The significance level, also denoted as alpha or α, is the probability of rejecting the null
hypothesis when it is true. For example, a significance level of 0.05 indicates a5% risk of
concluding that a difference exists when there is no actual difference.
TYPE I & TYPE II Error
When conducting a hypothesis test, we could:
 Reject the null hypothesis when there is a genuine effect in the population;
 Fail to reject the null hypothesis when there isn’t a genuine effect in the population.
However, as we are inferring results from samples and using probabilities to do so, we are never
working with 100% certainty of the presence or absence of an effect. There are two other
possible outcomes of a hypothesis test.
 Reject the null hypothesis when there isn’t a genuine effect – we have a false positive result
and this is called Type I error.
 Fail to reject the null hypothesis when there is a genuine effect – we have a false negative
result and this is called Type II error.
So in simple terms, a type I error is erroneously detecting an effect that is not present, while a
type II error is the failure to detect an effect that is present.
Type I error
This error occurs when we reject the null hypothesis when we should have retained it. That
means that we believe we found a genuine effect when in reality there isn’t one. The probability
of a type I error occurring is represented by α and as a convention the threshold is set at 0.05

(also known as significance level). When setting a threshold at 0.05 we are accepting that there
is a 5% probability of identifying an effect when actually there isn’t one.
Type II error
This error occurs when we fail to reject the null hypothesis. In other words, we believe that
there isn’t a genuine effect when actually there is one. The probability of a Type II error is
represented as β and this is related to the power of the test (power = 1- β). Cohen (1998)
proposed that the maximum accepted probability of a Type II error should be 20% (β = 0.2).
When designing and planning a study the researcher should decide the values of α and β,
bearing in mind that inferential statistics involve a balance between Type I and Type II errors. If
α is set at a very small value the researcher is more rigorous with the standards of rejection of
the null hypothesis. For example, if α = 0.01 the researcher is accepting a probability of 1% of
erroneously rejecting the null hypothesis, but there is an increase in the probability of a Type II
error.
In summary, we can see on the table the possible outcomes of a hypothesis test:
The 7 Step Process of Statistical Hypothesis
Testing
Step 1: State the Null Hypothesis
The null hypothesis can be thought of as the opposite of the "guess" the research made (in this
example the biologist thinks the plant height will be different for the fertilizers). So the null would
be that there will be no difference among the groups of plants. Specifically in more statistical
language the null for an ANOVA is that the means are the same. We state the Null hypothesis as:
H0:μ1=μ2=⋯=μk
for k levels of an experimental treatment.
Note: Why do we do this? Why not simply test the working hypothesis directly? The answer lies in
the Popperian Principle of Falsification. Karl Popper (a philosopher) discovered that we can’t

conclusively confirm a hypothesis, but we can conclusively negate one. So we set up a Null
hypothesis which is effectively the opposite of the working hypothesis. The hope is that based on
the strength of the data we will be able to negate or Reject the Null hypothesis and accept an
alternative hypothesis. In other words, we usually see the working hypothesis in HA.
Step 2: State the Alternative Hypothesis
HA: treatment level means not all equal
The reason we state the alternative hypothesis this way is that if the Null is rejected, there are many
possibilities.
For example, μ1≠μ2=⋯=μk is one possibility, as is μ1=μ2≠μ3=⋯=μk. Many people make the
mistake of stating the Alternative Hypothesis as: μ1≠μ2≠⋯≠μk which says that every mean differs
from every other mean. This is a possibility, but only one of many possibilities. To cover all
alternative outcomes, we resort to a verbal statement of ‘not all equal’ and then follow up with
mean comparisons to find out where differences among means exist. In our example, this means
that fertilizer 1 may result in plants that are really tall, but fertilizers 2, 3 and the plants with no
fertilizers don't differ from one another. A simpler way of thinking about this is that at least one
mean is different from all others.
Step 3: Set α
If we look at what can happen in a hypothesis test, we can construct the following contingency
table:
In Reality
Decision H0 is TRUE H0 is FALSE
Accept H0 OK
Type II Error
β = probability of Type II Error
Reject H0
Type I Error
α = probability of Type I Error
OK
You should be familiar with type I and type II errors from your introductory course. It is important
to note that we want to set α before the experiment (a-priori) because the Type I error is the more
‘grevious’ error to make. The typical value of α is 0.05, establishing a 95% confidence level. For
this course we will assume α =0.05.
Step 4: Collect Data
Remember the importance of recognizing whether data is collected through an experimental design
or observational.

Step 5: Calculate a test statistic
For categorical treatment level means, we use an F statistic, named after R.A. Fisher. We will
explore the mechanics of computing the Fstatistic beginning in Lesson 2. The F value we get from
the data is labeled Fcalculated.
Step 6: Construct Acceptance / Rejection regions
As with all other test statistics, a threshold (critical) value of F is established. This F value can be
obtained from statistical tables, and is referred to as Fcritical or Fα. As a reminder, this critical value
is the minimum value for the test statistic (in this case the F test) for us to be able to reject the null.
The F distribution, Fα, and the location of Acceptance / Rejection regions are shown in the graph
below:
Step 7: Based on steps 5 and 6, draw a conclusion about H0
If the Fcalculated from the data is larger than the Fα, then you are in the Rejection region and you can
reject the Null Hypothesis with (1-α) level of confidence.
Note that modern statistical software condenses step 6 and 7 by providing a p-value. The p-value
here is the probability of getting an Fcalculated even greater than what you observe. If by chance,
the Fcalculated = Fα, then the p-value would exactly equal to α. With larger Fcalculated values, we move
further into the rejection region and the p-value becomes less than α. So the decision rule is as
follows:
If the p-value obtained from the ANOVA is less than α, then Reject H0 and Accept HA.

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