The alternative hypothesis is a statement in statistical hypothesis testing that proposes a potential effect or relationship between variables, contrary to the null hypothesis which suggests no effect or relationship exists. It is what researchers aim to support through evidence gathered from data analysis, and its acceptance implies that the observed data is statistically significant.
A confidence interval is a range of values, derived from sample data, that is likely to contain the true population parameter with a specified level of confidence. It provides a measure of uncertainty around the estimate, allowing researchers to make inferences about the population with a known level of risk for error.
ANOVA, or Analysis of Variance, is a statistical method used to determine if there are significant differences between the means of three or more independent groups. It helps in understanding whether the observed variations between group means are due to actual differences or random chance.
Sequential testing is a statistical method that allows for the evaluation of data as it is collected, rather than waiting for all data to be gathered before analysis. This approach enables researchers to make decisions or adjust hypotheses in real-time, potentially reducing the number of observations needed and increasing the efficiency of the study.
Confidence intervals in diagnostic testing provide a range of values within which the true value of a diagnostic parameter, such as sensitivity or specificity, is expected to lie with a certain level of confidence, typically 95%. This statistical tool helps in understanding the precision and reliability of diagnostic tests, allowing healthcare professionals to make informed decisions based on the potential variability of test results.