Notes on formulating hypotheses for UGC NET Paper 1 Research Aptitude, in 3 short sub-topics. Each one gives the concept, the facts to memorise and the traps examiners set.
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Topic 11 of 29
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Hypotheses can be classified in several ways. A research (scientific) hypothesis states the expected relationship in substantive terms; a statistical hypothesis restates it in terms of population parameters, such as μ1 = μ2. A null hypothesis denies any difference or relationship, and an alternative hypothesis asserts one. A directional (one-tailed) hypothesis predicts the direction of the difference (group A will score higher than group B) and is tested with a one-tailed test; a non-directional (two-tailed) hypothesis predicts only that a difference exists. A simple hypothesis links one independent and one dependent variable, whereas a complex hypothesis involves several variables. A working hypothesis is a provisional guess used to guide early investigation.
The null hypothesis states that there is no significant difference or relationship, for example “there is no significant difference in the achievement of boys and girls”. It is the starting assumption of statistical testing, because it is easier to find evidence against a precise statement than to prove a vague one. The researcher computes a test statistic and its p-value; if p is smaller than the chosen significance level, H0 is rejected in favour of H1, otherwise we fail to reject it. Failing to reject is not proof that H0 is true, only that the data did not provide enough evidence against it. Typical forms are H0: μ1 = μ2 against H1: μ1 ≠ μ2 (two-tailed) or H1: μ1 > μ2 (one-tailed).
Hypotheses come from theory (deduced consequences), earlier research findings, everyday observation and experience, analogy with other fields, cultural beliefs and insight. Goode and Hatt list sources such as science itself, analogies, culture and personal experience. Formulating a hypothesis involves reviewing the literature, identifying the variables, stating the expected relationship, checking that it can be tested with available methods, and operationalising the terms. A good hypothesis is clear, specific, testable, consistent with known facts, simple, and stated before data are collected.
Memorise
Sources: theory, past research, observation, analogy, culture, experience.
Qualities: testable, specific, simple, consistent with existing knowledge.
Analyse / traps
A hypothesis built after looking at the data and then “tested” on the same data is poor practice.
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