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.