Introduction Hypothesis is assumption which is based on

Introduction • Hypothesis is assumption which is based on reasoning. Word “Hypothesis” is a combination of two words the first one is ‘Hypo’ (that means under or less than or tentative) and the second one is ‘thesis’ (that means General opinion or statement about solution of a problem) , So the word “Hypothesis” means tentative statement about solution of a problem or Hypothesis means the guesses to solve the research problem.

Hypothesis is a part of Research so before knowing or discussing more about hypothesis, we will discuss Legal Research and before dealing with legal research, we will discuss research.

The Meaning of Research is the way of gaining new knowledge in systematic way. Many Researchers have given various definition of research, these will be as follows: Red Man & Mory, “Research is a systematic effort to gain new knowledge”. M. H. Gopal, “A research is essentially a systematic enquiry seeking new facts through objectives. ” These definitions apparently portray research as a general process of enquiry.

Sources of Hypothesis According to Webster, “Hypothesis is a guess made by the researcher which either solve the problem or guide him in further investigation. ” The sources of Hypothesis are almost the same as problems. The major sources of the Hypothesis; • Reading Material- Published books, Journals, Magazines, Seminar Reports, • Principle of Theories – Rule of Law, Basic Structure etc. , • Personal Experience, and • Other Studies.

IMPORTANCE • It provides a tentative explanation of phenomena and facilitates the extension of knowledge in an area. • It provides the investigator with a relational statement that is directly testable in a research study. • It provides direction to the research. • It provides a framework for reporting conclusions of the study.

• It could be considered as the working instrument of theory. Hypotheses can be deduced from theory and from other hypotheses. • It could be tested and shown to be probably supported or not supported, apart from man’s own values and opinions.

• It guides the direction of the study: Quite frequently one comes across a situation when the researcher tries to collect all possible information on which he cou ld lay his hands on. Later on he may find that only part of it he could utilize. Hence there was an unnecessary use of resources on trivial concerns. In such a situation, hypothesis limits what shall be studied and what shall not be.

• It identifies facts that are relevant and those tha t are not: Who shall be studied (married • couples), in what context they shall be studied (th eir consumer decision making), and what shall be studied (their individual perceptions of their roles)

• It provides a framework for organizing the concl usions of the findings:

CHARACTERISTICS • Hypothesis should be simple • Hypothesis should be specific – A specific hypothesis leaves no ambiguity about the subjects and variables, or about how the test of statistical significance will be applied. It uses concise operational definitions that summarize the nature and source of the subjects and the approach to measuring variables.

• Hypothesis must be conceptually clear. The concepts used in the hypothesis should be clearly defined, operationally if possible. Such definitions should be commonly ac cepted and easily communicable among the research scholars

• Hypothesis should have empirical referents. The variables contained in the hypothesis should be empirical realities. In case these are not empiri cal realities then it will not be possible to make the observations. Being handicapped by the data collection, it may not be possible to test the hypothesis. Watch for words like ought, should, b ad

• Hypothesis should be related to available techniques of research. Hypothesis may have empirical reality; still we are looking for tools and techniques that could be used for the collection of data. If the techniques are not there then the researcher is handicapped. Therefore, either the techniques are already available or the researcher is in a position to develop suitable techniques for the study.

TYPES OF HYPOTHESIS NULL HYPOTHESIS ALTERNATIVE HYPOTHESIS DIRECTIONAL HYPOTHESIS NON-DIRECTIONAL HYPOTHESIS

An alternative hypothesis is a statement that suggests a potential outcome that the researcher may expect. (H 1 or HA) • Comes from prior literature or studies. • It is established only when a null hypothesis is rejected. • Often an alternative Hypothesis is the desired conclusion of the investigator. • The two types of alternative hypothesis are:

NULL HYPOTHESIS A null hypothesis is a statement that there is no actual relationship between variables. (HO ) ØA null hypothesis may read, “There is no difference between…. . ” ØHo states the opposite of what the experimenter would expect or predict. ØThe final conclusion of the investigator will either retain a null hypothesis or reject a null hypothesis in favor of a alternative hypothesis ØNot rejecting Ho does not really mean that Ho is true. There might not be enough evidence against Ho Ø“There is no significant difference in the anxiety level of children of High IQ and those of low IQ. ”

• These are used when the researcher believes there is no relationship between two variables or when there is inadequate theoretical or empirical information to state a research hypothesis.

DIRECTIONAL HYPOTHESIS Is a type of alternative hypothesis that specifies the direction of expected findings. · These are usually derived from theory. Sometimes directional hypothesis are created to examine the relationship among variables rather than to compare groups. Directional hypothesis may read, ”…is more than. . ”, “…will be lesser. . ” · They specify the expected direction of the relationship between variables i. e. the researcher predicts not only the existence of a relationship but also its nature. Example: “ Children with high IQ will exhibit more anxiety than children with low IQ

NON-DIRECTIONAL HYPOTHESIS Is a type of alternative hypothesis in which no definite direction of the expected findings is specified. · The researcher may not no what can be predicted from the past literature. · It may read, “. . there is a difference between. . ” · Example: “ There is a difference in the anxiety level of the children of high IQ and those of low IQ. ”

NON-DIRECTIONAL HYPOTHESIS IS Used when there is little or no theory, or when findings of previous studies are contradictory. Do not show the direction of the relationship.

• To infer is to deduce or conclude from facts and reasoning. • Inference is the use of inductive reasoning to move from a specific case to a general truth. • Thus, statistics are used to infer results from the specific study to a general statement about the larger population. • Inferential statistics are statistics that are designed to allow inference from a sample statistic to a population parameter. • They are commonly used to test a hypothesis of similarities and differences in subsets of the sample under study. • statistics

• Hypothesis is a formal statement that presents the expected relationship between an independent and dependent variable. (Creswell, 1994)

FORMULATION OF HYPOTHESIS • After you have reviewed the relevant literature and have a research question, you are prepared to be more specific. You want to make one or more predictions for your study. Such a prediction is called a hypothesis. It is an educated guess regarding what should happen in a particular situation under certain conditions. Not all studies require that you test a hypothesis; some may simply involve collecting information regarding an issue. For those that do have a hypothesis, the hypothesis should derive logically from previous findings or the predictions of a particular theory. Hypotheses should not be based simply on what the student believes should happen. A clear rationale is necessary.

Process for the formulating and developing of hypothesis There is specific process for the formulating or developing hypothesis. These process consist of four steps as follows Observation: It is the first stage of Hypothesis. Reflection: We see number of child marriages and also find uneducated people there. Now we anticipate a relation which is based on experience we now formulate a answer that child marriages common among uneducated people and this answer is Hypothesis. Deduction/Induction: In deduction thinking process goes from the general to specific, this process begins with general Hypothesis and proceeds towards specific Hypothesis. Induction is an approach which goes from specific to general. It begins with data and observation and proceeds toward Hypothesis. Verification: This is the last stage or even post Hypothesis formulation. In this we actually take our Hypothesis to solve our difficult problems.

Characteristics of Hypothesis A good Hypothesis is one which is incorporates following characteristics to a large extent. Non Contradictories – A hypothesis should be self-consistent and not contradictory. For example it is self-contradictory to believe that all law degree holders are lawyers because some of law degree holder may be a judge, teacher etc. Economical: Hypothesis should be parsimonious (carefulness in use of money) out of several hypothesis that should be performed which is more parsimonious. Simplest or general– A simplest or general hypothesis should be preferred and a good hypothesis can be done in simplest way. Capable of empirical test: Goode and Hatt suggested that the hypothesis should be such as can be put to empirical test which is the basis of objectivity.

The Function of Hypothesis It transforms research questions into testable propositions. It leads to discovery of additions to knowledge by helping to confirm or disconfirm particular theories or propositions. It determines the types of data needed for an inquiry and suggests the most appropriate instrument for data collection. It suggests the most appropriate methods and tools for the analysis of data. It provides the framework for drawing the conclusion of a research.

• Null and alternative hypotheses • The null hypothesis states that there is no association between the predictor and outcome variables in the population (There is no difference between tranquilizer habits of patients with attempted suicides and those of age- and sex- matched “control” patients hospitalized for other diagnoses). The null hypothesis is the formal basis for testing statistical significance. By starting with the proposition that there is no association, statistical tests can estimate the probability that an observed association could be due to chance. • The proposition that there is an association — that patients with attempted suicides will report different tranquilizer habits from those of the controls — is called the alternative hypothesis. The alternative hypothesis cannot be tested directly; it is accepted by exclusion if the test of statistical significance rejects the null hypothesis.

• One- and two-tailed alternative hypotheses • A one-tailed (or one-sided) hypothesis specifies the direction of the association between the predictor and outcome variables. The prediction that patients of attempted suicides will have a higher rate of use of tranquilizers than control patients is a one-tailed hypothesis. A two-tailed hypothesis states only that an association exists; it does not specify the direction. The prediction that patients with attempted suicides will have a different rate of tranquilizer use — either higher or lower than control patients — is a two-tailed hypothesis. (The word tails refers to the tail ends of the statistical distribution such as the familiar bell-shaped normal curve that is used to test a hypothesis.

One tail represents a positive effect or association; the other, a negative effect. ) A one-tailed hypothesis has the statistical advantage of permitting a smaller sample size as compared to that permissible by a two-tailed hypothesis. Unfortunately, one-tailed hypotheses are not always appropriate; in fact, some investigators believe that they should never be used. However, they are appropriate when only one direction for the association is important or biologically meaningful. An example is the one-sided hypothesis that a drug has a greater frequency of side effects than a placebo; the possibility that the drug has fewer side effects than the placebo is not worth testing. Whatever strategy is used, it should be stated in advance; otherwise, it would lack statistical rigor. Data dredging after it has been collected and post hoc deciding to change over to one-tailed hypothesis testing to reduce the sample size and P value are indicative of lack of scientific integrity.

• • • • Null Hypothesis It is used for testing the hypothesis formulated by the researcher. Researchers treat evidence that supports a hypothesis differently from the evidence that opposes it. They give negative evidence more importance than to the positive one. It is because the negative evidence tarnishes the hypothesis. It shows that the predictions made by the hypothesis are wrong. The null hypothesis simply states that there is no relationship between the variables or the relationship between the variables is "zero. " That is how symbolically null hypothesis is denoted as "H 0". For example: H 0 = There is no relationship between the level of job commitment and the level of efficiency. Or H 0 = The relationship between level of job commitment and the level of efficiency is zero. Or The two variables are independent of each other. It does not take into consideration the direction of association (i. e. H 0 is non directional), which may be a second step in testing the hypothesis. First we look whether or not there is an association then we go for the direction of association and the strength of association. Experts recommend that we test our hypothesis indirectly by testing the null hypothesis. In case we have any credibility in our hypothesis then the research data should reject the null hypothesis. Rejection of the null hypothesis leads to the acceptance of the alternative hypothesis

• Alternative Hypothesis • The alternative (to the null) hypothesis simply states that there is a relationship between the variables • under study. In our example it could be: there is a relationship between the level of job commitment and • the level of efficiency. Not only there is an association between the two variables under study but also the relationship is perfect which is indicated by the number "1". Thereby the alternative hypothesis is • symbolically denoted as "H 1". It can be written like this: • H 1: There is a relationship between the level of job commitment of the officers and their level of • efficiency

• Research Hypothesis • Research hypothesis is the actual hypothesis formulated by the researcher which may also suggest the • nature of relationship i. e. the direction of relationship. In our example it could be: • Level of job commitment of the officers is positively associated with their leve l of efficiency

• 1. Define the research hypothesis for the study. • 2. Explain how you are going to operationalize (that is, measure or operationally define) what you are studying and set out thevariables to be studied. • 3. Set out the null and alternative hypothesis (or more than one hypothesis; in other words, a number of hypotheses). • 4. Set the significance level.

• 5. Make a one- or two-tailed prediction. • 6. Determine whether the distribution that you are studying is normal (this has implications for the types of statistical tests that you can run on your data). • 7. Select an appropriate statistical test based on the variables you have defined and whether the distribution is normal or not. • 8. Run the statistical tests on your data and interpret the output. • 9. Reject or fail to reject the null hypothesis.

The null and alternative hypothesis • In order to undertake hypothesis testing you need to express your research hypothesis as a null and alternative hypothesis. The null hypothesis and alternative hypothesis are statements regarding the differences or effects that occur in the population. You will use your sample to test which statement (i. e. , the null hypothesis or alternative hypothesis) is most likely (although technically, you test the evidence against the null hypothesis). So, with respect to our teaching example, the null and alternative hypothesis will reflect statements about all statistics students on graduate management courses.

Significance levels • The level of statistical significance is often expressed as the so-called p-value. Depending on the statistical test you have chosen, you will calculate a probability (i. e. , the p-value) of observing your sample results (or more extreme) given that the null hypothesis is true.

• So, you might get a p-value such as 0. 03 (i. e. , p =. 03). This means that there is a 3% chance of finding a difference as large as (or larger than) the one in your study given that the null hypothesis is true. However, you want to know whether this is "statistically significant". Typically, if there was a 5% or less chance (5 times in 100 or less) that the difference in the mean exam performance between the two teaching methods (or whatever statistic you are using) is as different as observed given the null hypothesis is true, you would reject the null hypothesis and accept the alternative hypothesis.

• Alternately, if the chance was greater than 5% (5 times in 100 or more), you would fail to reject the null hypothesis and would not accept the alternative hypothesis. As such, in this example where p =. 03, we would reject the null hypothesis and accept the alternative hypothesis. We reject it because at a significance level of 0. 03 (i. e. , less than a 5% chance), the result we obtained could happen too frequently for us to be confident that it was the two teaching methods that had an effect on exam performance.

One- and two-tailed predictions • When considering whether we reject the null hypothesis and accept the alternative hypothesis, we need to consider the direction of the alternative hypothesis statement. For example, the alternative hypothesis that was stated earlier is: • Alternative Hypothesis (HA): Undertaking seminar classes has a positive effect on students' performance.

• The alternative hypothesis tells us two things. First, what predictions did we make about the effect of the independent variable(s) on the dependent variable(s)? Second, what was the predicted direction of this effect? Let's use our example to highlight these two points.

• Sarah predicted that her teaching method (independent variable: teaching method), whereby she not only required her students to attend lectures, but also seminars, would have a positive effect (that is, increased) students' performance (dependent variable: exam marks). If an alternative hypothesis has a direction (and this is how you want to test it), the hypothesis is one-tailed. That is, it predicts direction of the effect. If the alternative hypothesis has stated that the effect was expected to be negative, this is also a one-tailed hypothesis.

• Alternatively, a two-tailed prediction means that we do not make a choice over the direction that the effect of the experiment takes. Rather, it simply implies that the effect could be negative or positive. If Sarah had made a two-tailed prediction, the alternative hypothesis might have been: • Alternative Hypothesis (Ha): Undertaking seminar classes has an effect on students' performance.

Rejecting or failing to reject the null hypothesis • let's return finally to the question of whether we reject or fail to reject the null hypothesis. • If our statistical analysis shows that the significance level is below the cut-off value we have set (e. g. , either 0. 05 or 0. 01), we reject the null hypothesis and accept the alternative hypothesis. Alternatively, if the significance level is above the cut-off value, we fail to reject the null hypothesis and cannot accept the alternative hypothesis. You should note that you cannot accept the null hypothesis, but only find evidence against it.

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