Inferential statistics help make inferences or predictions about a population based on a sample. These are especially important when the full population cannot be studied.
- Hypothesis Testing:
- Null Hypothesis (H0): The assumption that there is no effect or difference.
- Example: "There is no difference in recovery times between two surgical techniques."
- Alternative Hypothesis (H1): The hypothesis that there is an effect or difference.
- Example: "There is a difference in recovery times between the two techniques."
- P-value: A measure that helps you determine the significance of your results. A p-value less than 0.05 typically indicates statistical significance (i.e., the results are unlikely to have occurred by chance).
- Null Hypothesis (H0): The assumption that there is no effect or difference.
- Confidence Intervals (CI): A range of values that is likely to contain the true population parameter. For example, a 95% confidence interval means we are 95% confident that the true value lies within the range.
- Example: A study might find that the average recovery time for a surgery technique is 6 days with a 95% CI of 5-7 days. This means that, based on the sample, the true average recovery time for the population is likely between 5 and 7 days.