Hypothesis

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HYPOTHESIS: The word hypothesis is made up of two Greek roots ὑπόθεσις, its plural is hypotheses which is a proposed explanation for an observable phenomenon. The term derives from the Greek, ὑποτιθέναι – hypotithenai meaning "to put under" or "to suppose." which mean that it is some sort of ‘sub-statements’, for it is the presumptive statement of a proposition, which the investigation seeks to improve. The scientists observe the man of special class of phenomena and broads over it until by a flash of insight he perceives an order and intelligent harmony in it. This is often referred to as an ‘explanation’ of the facts he has observed. He has a ‘theory’ about particular mass of fact.

This theory when stated testable proposition formally and clearly subjected to empirical or experimental verification is known as hypothesis. The hypothesis furnishes the germinal basis of the whole investigation and remains to the end of its corner stone, for the whole research is directed to test it out by facts. At the start of investigation the hypothesis is a stimulus to critical thoughts offers insights into the confusion of phenomena. At the end it comes to prominence as the proposition to be accepted or rejected in the light of the findings. The word hypothesis consists of two words:

Hypo + Thesis = Hypothesis

‘Hypo’ means tentative or subject to the verification. “Thesis” means statement about solution of a problem.

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The word meaning of the term hypothesis is a tentative statement about the solution of the problem. Hypothesis offers a solution of the problem that is to be verified empirically and based on some rationale.

Another meaning of the word hypothesis which is composed of two words:-‘Hypo’ means composition of two or more variables which is to be verified. ‘Thesis’ means position of these variables in the specific frame of reference.

This is the operational meaning of the term hypothesis. Hypothesis is the composition of some variables i.e. to be verified empirically. It is a proposition about the factual and conceptual elements. Hypothesis is called a leap into the dark. It is a brilliant guess about the solution of the problem.

A tentative generalization or theory formulated about the character of phenomena under observation are called hypothesis. It is a statement temporarily accepted as true in the light of what is known at the time about the phenomena. It is the basis for planning and action in the research for new truth.

The second most important consideration in the formulation of the research problem is the construction of hypothesis. Hypotheses bring clarity specificity and focus to a research problem but are not essential for a study. You can conduct a valid investigation without constructing a single formal hypothesis. On the other hand, within the context research study you can construct as many hypotheses as you consider to be appropriate. Some believe that one must formulate a hypothesis

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to undertake an investigation. Hypotheses primarily arise from a set of hunches that are tested through a study and one can conduct a perfectly valid study without having these hunches or speculation. However, in epidemiological studies to narrow the field of investigation, it is important to formulae hypothesis.

The importance of hypothesis lies in their ability to bring direction, specificity and focus to a research study. They tell a research what specific information to collect, and thereby provide greater focus.

Let us imagine you are at the race sand you place a bet on the hunch that a particular horse will win. You will only know if your hunch was right after the race.

Hypotheses are based upon similar logics. As a researcher you do not know about the phenomenon, a situation, the prevalence of a condition in a population or about the outcome of a program, but you do have a hunch to form the basis of certain assumptions or guesses. You test these by collecting information that will enable you to conclude if your hunch was right. The verification process can have one of the three outcomes. Your hunch may prove to be:

1) Right; 2) Partially right; or 3) Wrong

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Without this process of verification, you cannot include anything about the validity of your assumption.

Hence a hypothesis is a hunch, assumption, suspicion, assertion or an ides about a phenomenon, relationship or situation, the realty or truth which you do not know. A researcher calls these assumptions, assertions, statements or hunches hypotheses and they become the basis of an inquiry.

Best (1986), states the research or scientific hypothesis is a formal affirmative statement predicting a single research outcome, a tentative explanation of the relationship between two or more variable. For the hypothesis to be testable, the variables must be operationally defined. That is, the researcher specifies what operations were concluded, or tests used, to measure each variable. Thus hypothesis focuses the investigation on a definite target and determines what observations, or measures, are to be used.

A better understanding of the hypothesis could be had by taking note of the following definition: a hypothesis is a suggested solution to a problem. A hypothesis consists of elements expressed in an orderly system of relationships which seek to explain a condition that has not yet been verified by facts. In a hypothesis, some of the elements or relationship between the element are known facts. But other elements or relationships are conceptual. That is, they arte product of the research worker’s imagination. They leap beyond known facts to intelligent guesses about unknown conditions in an effort to extend or enlarge our knowledge. The conceptual and factual elements and relationships must be

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formulated in such a precise and objective manner that the research worker can test the implications of the hypothesis.

Definitions of hypothesis: The term hypothesis has been defined in several ways. Some important definitions are given below

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“It is a tentative supposition or provisional guess which seems to explain the situation under observation.” -James E.

Greighton

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“A hypothesis is a tentative generalization the validity of which remains to be tested. In its most elementary stage the hypothesis may be a hunch, guess, imaginative idea which becomes the basis for further investigation.” Lungerg

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“It is a shrewd guess or inference that is formulated and provisionally adopted to explain observed facts or conditions and to guide in further investigation.” John W. Best

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“A hypothesis then could be defined as an expectation about events based on generalization of assumed relationships between the variables.” Bruce W.Tuckman

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“A hypothesis is a statement temporarily accepted as true in the light of what is, at the time, known about a phenomenon, and it is employed as a basis for action in the search for the truth, when the hypothesis is completely established, it may take the form of facts, principles, theories.” Barr and Scates

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Sources of Hypothesis:

Hypotheses are oriented originally forms the same background that serves to reveal the problem. The sources are basically theoretical background, knowledge, insight and imagination that come from industrial programme and wide reading experiences, familiarity with existing practices. The major sources of hypothesis are given below:

1) Specialization of an educational field. 2) Programme of reading: published studies, abstracts research journals. Handbooks, seminars on the issue, current research on the current issue. 3) Instructional programme persuaded. 4) Analysis of the area studied. 5) Considering existing practices and needs. 6) Extension of the investigation. And 7) Offshoots of the research in the field

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The functions of a hypothesis:

While some researchers believe that to conduct a study requires a hypothesis, having a hypothesis is not essential as already mentioned. However a hypothesis is important in terms of bringing clarity to the research problem.

Phase 1 Formulate your hunch or assumption

Phase 2 Collect the required data

Phase 3 Analyse data to draw conclusions about the hunch true or false

The formulation of hypotheses provides a study with focus. It tells you what specific aspects of a research problem to investigate.

A hypothesis tells you what data to collect and what not to collect, thereby providing focus to the study. As it provides a focus, construction of hypotheses enhances objectivity in a study. A hypothesis may enable you to add to the formulation of theory. It enables you to specifically conclude what is true or what is false.

The following are the main functions of hypothesis in the research process suggested by H.H Mc. Ahsan:

1) It is the temporary solution of the problem concerning with some truth which enables an investigator to start his research works.

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2) It offers a basis in establishing the specifics what to study for and many provide possible solutions to the problem. 3) Each hypothesis may lead to formulate another hypothesis. 4) A preliminary hypothesis may take the form off final hypothesis. 5) Each hypothesis provides the investigator with definite statement which may be objectivity tested and accepted or rejected and deals for interrupting results and drawings conclusion that is related to original purpose.

The functions of hypothesis may be condensed into three. The following are the threefold functions of a hypothesis:

1) To delimit the field of the investigation. 2) To sensitize the researcher so that he should work selectively, and have very realistic approach to the problem. 3) To offer the simple means for collecting evidences to the verification.

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Nature of Hypothesis: The following are the main features of hypothesis:

1) It is conceptual in nature. Some kind of conceptual elements in the framework are involved in the hypothesis. 2) It is a verbal statement in declarative form. It is a verbal expression of ideas and concepts, it is not merely idea but in the verbal form, the idea is ready enough for verification. 3) It has the empirical referent. A hypothesis contains some empirical referent. It indicates the tentative relationship between two or more variables. 4) It has a forward or future reference. A hypothesis is future oriented. It relates to the future verification not the past facts and information. 5) It is the pivot of scientific research. All the research activities are designed for its verification.

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Characteristics of Good Hypothesis: A good hypothesis must have the following main hypothesis:

1) A good hypothesis is in agreement with the observed facts. 2) A good hypothesis does not conflict with any law of nature which is known to be true. 3) A good hypothesis is started in a simplest possible term. 4) A good hypothesis permits of the application of deductive reasoning. 5) A good hypothesis shows very clear verbalization. It is different from what we generally called hunch. 6) A good hypothesis ensures that the methods of verification are under the control of investigator. 7) A good hypothesis guarantees that available tools and techniques will be effectively used for the purpose of verification. 8) A good hypothesis takes into account the different types controls which are to be exercised for the purpose of verification. 9) A good hypothesis ensures that the sample is easily approachable. 10) A good hypothesis shows clearly the role of each variable used in the study. 11) A good hypothesis maintains a very apparent distinction with what is called theory law, facts, assumptions and postulates.

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If-then statement: As already stated a hypothesis is a testable statement of the relationship among variables. A hypothesis can also test whether there are differences between two groups. To examine whether or not the conjectured relationships or differences exist, these hypothesis can be set either as propositions or in the form of IF-THEN statements.

TYPES OF HYPOTHESIS: Following are the main types of hypothesis •

Null Hypothesis.

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Alternate Hypothesis.

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Directional Hypothesis.

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Non directional Hypothesis.

Null Hypothesis:

The null hypothesis is a proposition that states a definite, exact relationship between two variables. That is, it states the population correlation between two variables is equal to zero or that the difference in the means of two groups in the population is equal to zero. In general, the null statement is expressed as no significant relationship between two variables or no significant difference between two groups. The null hypothesis is the hypothesis that states that there is no relation between the phenomena whose relation is under investigation, or at least not of the form given by the alternative hypothesis.

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To explain it further, in setting up null hypothesis, we are stating that there is no difference between what we might find in a population characteristics and the sample we are studying. Since we do not know the true state of affairs in the population, all we can do is draw inferences based on what we find in our samples. What we imply through the null hypothesis is that any differences found between two sample groups or any relationship found between two variables based on our sample is simply due to random sampling fluctuations and not due to any true difference between the two population groups, or relationship between two variables. The null hypothesis is thus formulated so that it can be tested for possible rejection. If we reject the null hypothesis, then all permissible alternative hypotheses relating to the particular relationship tested could be supported. It is the theory that allows us to have faith in the alternative hypothesis that is generated in the particular research investigation. This is one more reason why theoretical framework should be grounded on sound, defendable logic to start with. Otherwise other researchers are likely to refuse and postulate other defensible explanations through different alternative hypothesis.

Example: One may wish to compare the test scores of two random samples of men and women, and ask whether the mean score of one population-group differs from the other. A null hypothesis would be that the mean score of the male population was the same as the mean score of the female population:

H0:μ1 = μ2 Where:

H0 = the null hypothesis

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μ1 = the mean of population 1, and μ2 = the mean of population 2. Alternatively, the null hypothesis may postulate (suggest) that the two samples are drawn from the same population, thus the variance and shape of the distributions would be equal, likewise the mean values. Formulation of the null hypothesis is a vital step in testing statistical significance. One can then establish the probability of observing the obtained data (or data more different from the prediction of the null hypothesis) if the null hypothesis is true. That probability is commonly named the "significance level" of the results. That is, in scientific experimental design, one may predict that a particular factor will produce an effect on our dependent variable — this is the alternative hypothesis. We then consider how often we would expect to observe our experimental results or results even more extreme, if we were to take many samples from a population in which there was no effect (i.e. we test against our null hypothesis). If we find that this happens rarely (up to, say, 5% of the time), we can conclude that our results support our experimental prediction — we reject our null hypothesis.

Alternate Hypothesis: The alternative hypothesis, as the name suggests, is the alternative to the null hypothesis: it states that there is some kind of relation. The alternative hypothesis may take several forms, depending on the nature of the hypothesized relation; in particular, it can be two-sided (for example: there is some effect, in a yet unknown direction) or one-sided (the direction of the hypothesized relation, positive or negative, is fixed in advance).

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Alternative hypothesis is the "hypothesis that the restriction or set of restrictions to be tested does NOT hold." Often denoted H1. In statistical hypothesis testing, the alternative hypothesis (or maintained hypothesis or research hypothesis) and the null hypothesis are the two rival hypotheses which are compared by a statistical hypothesis test. An example might be where water quality in a stream has been observed over many years and a test is made of the null hypothesis that there is no change in quality between the first and second halves of the data against the alternative hypothesis that the quality is poorer in the second half of the record. Modern statistical hypothesis testing accommodates this type of test since the alternative hypothesis can be just the negation of the null hypothesis.

Example: The alternative hypothesis, H1, is a statement of what a statistical hypothesis test is set up to establish. For example, in a clinical trial of a new drug, 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 alternative hypothesis might also be that the new drug is better, on average, than the current drug. In this case we would write H1: the new drug is better than the current drug, on average. 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".

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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.

Directional Hypothesis: Those hypothesis who, in stating the relationship between two variables or comparing two groups, terms such as positive, negative, more than, less than, and the like are used then these hypothesis are directional because the direction of the relationship between the variables (positive/negative) is indicated or the nature of the difference between two groups on a variable (more than/less than) is postulated. A directional hypothesis is also called a one tailed hypothesis.

Example: 1. The greater the stress experienced in the job, the lower the job satisfaction of employees. 2. Women are motivated than men.

Non Directional Hypothesis: Those hypothesis that do postulate a relationship or difference, but offer no indication of the direction of these relationships and differences are called non directional hypothesis.

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In other words, though it may be conjectured that there would be a significant relationship between two variables , we may not be able to say whether the would be positive or negative. Likewise, even if we can conjecture that there will be differences between two groups on a particular variable, we will not be able to say which group will be more and which less on that variable. Non directional hypothesis are formulated either because the relationships or differences have never been previously explored and hence there is no basis for indicating the direction or because there have been conflicting findings in previous research studies on the variables. In some studies a positive relationship might have been found, while in others a negative relationship might have been traced. Hence the current researcher might only be able to hypothesize that there would be a significant relationship, but the direction may not be clear. In such case, the hypothesis could be stated non directionally. A non directional hypothesis is also called a two tailed hypothesis.

Examples: 1. There is a relationship between age and job satisfaction. 2. There is a difference between the work ethic values of American and Asian employees. Before concluding the discussion on hypothesis, it has to be reiterated that hypothesis generation and testing can be done both through deduction and induction.  In deduction, the theoretical model is first developed, testable hypothesis are then formulated, and data collected and then the hypothesis are tested.

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 In the inductive process, new hypothesis are formulated based on what is known from the data already collected, which are then tested.

In sum, new hypothesis not originally thought of or which have been previously untested might be developed after data are collected. Creative insights might compel researchers to test a new hypothesis from existing data, which, if substantiated, would add new knowledge and help theory building. Through the enlargement of our understanding of the dynamics operating in different situations using the deductive and the inductive processes, we add to the total body of the knowledge in the area.

Steps in Hypothesis Testing: The steps to be followed in hypothesis testing are: 1. State the null and alternative hypothesis.

2. Choose the appropriate statistical test depending on whether the data collected are parametric or non parametric.

3. Determine the level of significance required.,

4. See if the output results from computer analysis indicate that the significance level is met. This critical value demarcates the region of rejection from that of acceptance of the null hypothesis.

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5. When the resultant value is larger than the critical value, the null hypothesis is rejected and the alternate accepted. If the calculated value is less than the critical value, the null is accepted and the alternate rejected.

HYPOTHESIS TESTING WITH QUALITATIVE RESEARCH: NEGATIVE CASE ANALYSIS

Hypothesis can also be tested with qualitative data. For example, let us say that a researcher has developed the theoretical framework after extensive interviews that unethical practices by employees are a function of their inability to discriminate between right and wrong, or due to a dire need for more money or the organizations indifferences to such practices. To test the hypothesis that these three factors are the primary ones that influence unethical practices, the researcher would look for data that could refute the hypothesis. When even a single case does not support the hypothesis, the theory could be revised. Let us say that researcher has find a case where an individual is deliberately engaged in the unethical practice of accepting kickbacks (despite the fact that he was knowledgeable enough to discriminate between right and wrong, was not in the need of money, and knew that the

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organization would not be indifferent to his behavior) simply he wanted to get back at the system, which would not listen to his advice. This new discovery through disconfirmation of the original hypothesis, known as negative case method, enables the researcher to revise the theory and the hypothesis until such time as the theory becomes robust.

Summary: Hypotheses, though important, are not essential for a study. A perfectly valid study can be conducted without constructing single hypotheses. Hypotheses are important for bringing clarity, specificity and focus to a research study.

A hypothesis is a speculative statement that is subjected to verification through a research study. In formulating a hypothesis it is essential to make sure that it is simple, specific and conceptually clear; is able to be verified; is rooted in an existed body of knowledge; and able to be operationalized.

The testing of the hypothesis becomes meaningless if anyone of the aspect of your study, design, sampling procedures, method of data collection, analysis of data,

Hypothesis statistical procedures applied or conclusive drawn is faulty or inappropriate this can result in erroneous verification of hypothesis.

REFRENCES: Yogesh Kumar & Ruchika Nath. Research Methodology Uma Sekaran. Research Methods for Business A Skill Building Approach Fourth Edition Retrived 2 April 2, 2010 from http://www.wikipedia.org/ Dr. Tariq H. Malik. Meliorism of research Methodology http://www.stats.gla.ac.uk/steps/glossary/hypothesis_testing.html#h1

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