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. Onetailed: When the majority of the population is concentrated on one side,(the sample test is either higher or lower than the population parameter).
4. Twotailed: the critical distribution of the population is twosided. 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.