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Hypothesis Testing

The pillar of true research findings

a key component in hypothesis testing


  • An Assumption, is an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.
  • Shows how two or more variables are expected to relate to one another.

What is Hypothesis Testing?

  • An act in statistics whereby an analyst tests an assumption regarding a population parameter.
  • It ascertains whether a particular assumption is true for the whole population.
  • A technique that helps to determine whether a specific treatment has an effect on the individuals in a population.
  • Used to assess the plausibility of a hypothesis by using sample data.
  • If sample data are not consistent with the statistical hypothesis, the hypothesis is rejected.
  • The general goal is To rule out chance (sampling error) as a plausible explanation for the results of a research study.
  • The purpose of hypothesis testing is to test whether the null hypothesis (there is no difference, no effect) can be rejected or approved.
  • If the null hypothesis is rejected, then the research hypothesis can be accepted. If the null hypothesis is accepted, then the research hypothesis is rejected.
    • to decide between two explanations:

    I.The difference between the sample and the population can be explained by sampling error (there does not appear to be a treatment effect)

    II.The difference between the sample and the population is too large to be explained by sampling error (there does appear to be a treatment effect).

    • Hypothesis Testing Types

      1. Simple: the population parameter is stated as a specific value, making the analysis easier.

      2. Composite: the population parameter ranges between a lower and upper value.

      3. One-tailed: When the majority of the population is concentrated on one side,(the sample test is either higher or lower than the population parameter).

      4. Two-tailed: the critical distribution of the population is two-sided. Here the test sample is either higher or lower than a number of given values.

      Relevance and Use of Hypothesis Testing

    • Validates a theory with the help of systematic statistical inference.
    • Researchers try to reject the null hypothesis in order to validate the alternate explanation.
    • Widely applied in psychology, biology, medicine, finance, production, marketing, advertising, and criminal trials.
  • Limitations of Hypothesis Testing

  • It is all about assumptions and interpretations.
  • It, therefore, requires superior analytical abilities.
  • As a result, it is inaccessible for most.
  • This method heavily relies on mere probability.
  • There can be errors in data.
  • For smaller sample sets, this approach may not be the most suitable.

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