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RESEARCH HYPOTHESIS

Hypothesis is defined as a tentative statement or assumption of an event. A hypothesis is a prediction of a relationship between one or more factors and the problem under study that can be tested. Hypotheses can take various forms, depending on the question being asked and the type of study being conducted. A key feature of all hypotheses is that each must make a prediction. These predictions are then tested by gathering and analyzing data, and the hypotheses can either be supported or refuted on the basis of the data.CHARACTERISTICS OF A GOOD HYPOTHESIS

Hypothesis should be simple. Hypothesis should be specific. Hypothesis should be stated in advance. Hypothesis should be clear and precise. Hypothesis should be capable of being tested. Hypothesis should state relationship between variables, if it happens to be a relational hypothesis. Hypothesis should be consistent with most known facts i.e., it must be consistent with a substantial body of established facts. Hypothesis should be amenable to testing within a reasonable time. Hypothesis must explain the facts that give rise to the need for explanation. Hypothesis must actually explain what it claims to explain, it should have empirical reference.

STEPS IN HYPOTHESIS TESTING

The basic logic of hypothesis testing is to prove or disprove the research question. By only allowing an error of 5% or 1% and making correct decisions based on statistical principles, the researcher can conclude that the result must be real if chance alone could produce the same result only 5% of the time or less. These five steps consists of all the decisions a researcher needs to make in order to answer any research question using an inferential statistical test.

The basic logic of hypothesis testing has been presented somewhat informally in the sections on Ruling out chance as and explanation and the "Null hypothesis." In this section the logic will be presented in more detail and more formally.

Calculate the probability that sample result would diverge as widely as it has from expectations, if H0 were true.State H0 as well as HASpecify the level if significance (or the value)Decide the correct sampling distributionSample a random sample(s) and workout an appropriate value from sample data

Is this probability equal or smaller than value in case of one tailed test and /2 in case of two tailed test.Reject H0Accept H0Thereby run the risk of committing Type I errorThereby run the risk of committing Type II error

1. The first step in hypothesis testing is to specify the null hypothesis (H0) and the alternative hypothesis (HA). If one hypothesis is true, the other is false. Alternatively, if one hypothesis is false or rejected, then the other is true or accepted. These two hypotheses are1. Null Hypothesis2. Alternate hypothesisThe null hypothesis would most likely be that there is no difference between methods (H0: 1 - 2 = 0). The alternative hypothesis would be H1: 1 2. If the research concerned the correlation between grades and SAT scores, the null hypothesis would most likely be that there is no correlation (H0: = 0). The alternative hypothesis would be H1: 0.2. The next step is to select a significance level. Typically the 0.05 or the 0.01 level is used.3. The third step is to calculate a statistic analogous to the parameter specified by the null hypothesis. If the null hypothesis were defined by the parameter 1- 2, then the statistic M1 - M2 would be computed.4. The fourth step is to calculate the probability value (often called the p value). The p value is the probability of obtaining a statistic as different or more different from the parameter specified in the null hypothesis as the statistic computed from the data. The calculations are made assuming that the null hypothesis is true. (click here for a concrete example)5. The probability value computed in Step 4 is compared with the significance level chosen in Step 2. If the probability is less than or equal to the significance level, then the null hypothesis is rejected; if the probability is greater than the significance level then the null hypothesis is not rejected. When the null hypothesis is rejected, the outcome is said to be "statistically significant" when the null hypothesis is not rejected then the outcome is said be "not statistically significant."6. If the outcome is statistically significant, then the null hypothesis is rejected in favor of the alternative hypothesis. If the rejected null hypothesis were that 1- 2 = 0, then the alternative hypothesis would be that 1 2. If M1 were greater than M2 then the researcher would naturally conclude that 1 2.

7. The final step is to describe the result and the statistical conclusion in an understandable way. Be sure to present the descriptive statistics as well as whether the effect was significant or not. ERRORS IN HYPOTHESIS TESTINGThere are two types of errors in hypothesis testing. i.e.1. Hypothesis is rejected when it is true called TYPE I error and is denoted by (alpha)2. Hypothesis is accepted when it is false is called TYPE II error denoted by (beta)Decision

Accept H0Reject H0

H0 (true)Correct decisionType I error ( error)

H0 (false)Type II error ( error)Correct decision

The probability of Type I error is usually determined in advance and is understood as the level of significance of testing the hypothesis. If type I error is fixed at 5%, it means that there are about 5 chances in 100 that we will reject H0 when H0.