Simply explained, a hypothesis is an educated guess. Hypothesis play a pivotal role in facilitating decision-making, given we live in a data-driven age. Hypothesis testing is a structured approach for determining whether the findings of a study provide sufficient evidence to support a specific theory relevant to a larger population. A hypothesis test assesses how unusual the result is, and where it is reasonable chance variation or whether the result is too extreme to be regarded as chance variation.
Primarily, hypothesis testing seeks to test whether the null hypothesis can be rejected or approved. In the event it is rejected, the alternative hypothesis can be accepted. If the null hypothesis is accepted, it implies the alternative hypothesis is rejected. Thus, a value is set in order to gauge whether the null hypothesis is accepted or rejected, and whether the result is statistically significant.
Process of Hypothesis Testing.
The hypothesis testing process is classified into different phases:
1.Restate the research question as the alternative hypothesis, and null hypothesis about the population.
- The null hypothesis states that there is no effect or difference, which is the hypothesis one attempts to reject with the test.
- The alternative hypothesis is that which is being tested, expressed as a correlation or statistical relationship between variables.
- Determine the significance level, often denoted by alpha (α). It implies the probability of rejecting the null hypothesis when it is true. The p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. A smaller p-value increases the likelihood for the alternative hypothesis being correct, and the greater the significance of the results.
One-sided vs. Two-sided Testing
Sampling
Why Hypothesis Testing?
It helps in estimating the sampling error, and factor it into the test results, facilitating effective decision-making.
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