The Relationship Between Confidence Intervals and Hypothesis Testing
What is the relationship between Confidence Intervals and Hypothesis Testing?
Answer:
Inferential statistics frequently combines the use of confidence intervals with hypothesis testing, two statistical concepts that are closely connected.
A confidence interval is a range of values that, with a given degree of certainty, offers an estimate of the real population parameter. Based on sample data, it is frequently used to calculate an unknown population's mean or percentage.
Hypothesis testing is a statistical technique used to draw conclusions or take action regarding a population from a small sample of data. Making a null hypothesis (H0) and an alternative hypothesis (Ha) as well as doing statistical tests are required.
While hypothesis testing determines the likelihood of accepting or rejecting a null hypothesis, the confidence intervals offer estimates of population parameters.
Confidence Intervals
Confidence intervals provide a range within which the true population parameter is likely to fall. It is calculated based on sample data and a certain level of confidence chosen by the researcher. For example, a 95% confidence interval means there is a 95% chance that the true population parameter lies within the interval.
Hypothesis Testing
Hypothesis testing involves making assumptions about a population parameter and using sample data to either accept or reject the null hypothesis. The null hypothesis assumes no significant difference or effect, while the alternative hypothesis suggests that there is a significant difference or effect in the population.
Relationship Between Confidence Intervals and Hypothesis Testing
The relationship between confidence intervals and hypothesis testing lies in how they are both used to make inferences about populations based on sample data. While confidence intervals provide a range of values within which the population parameter is likely to lie, hypothesis testing evaluates whether there is enough evidence to reject the null hypothesis in favor of the alternative.