9.4.14 Hypothesis Testing

Describes What is Meant by Hypothesis Testing (Null and Alternative Hypotheses)

Hypothesis testing is a statistical method used to make decisions about population parameters based on sample statistics. It involves two competing hypotheses: the null hypothesis (H0) and the alternative hypothesis (Ha).

The null hypothesis states that there is no significant difference or relationship between the two variables, while the alternative hypothesis suggests that there is a significant difference or relationship. The goal of hypothesis testing is to reject the null hypothesis in favour of the alternative hypothesis based on statistical evidence.

To illustrate the concept of hypothesis testing, let us consider a clinical example. Suppose we are interested in determining whether a new drug is effective in reducing blood pressure in patients with hypertension. We could formulate our null hypothesis as follows: the new drug has no effect on blood pressure. The alternative hypothesis would be that the new drug does have an effect on blood pressure.

To test our hypotheses, we would need to collect data from a sample of patients and compare the mean blood pressure of those who received the new drug with the mean blood pressure of those who received a placebo. We would then calculate a test statistic, such as a t-test or a z-test, which would help us determine the probability of obtaining our observed difference in means under the null hypothesis.

If the probability, or p-value, is lower than our chosen level of significance (typically 0.05), we would reject the null hypothesis and conclude that the new drug is effective in reducing blood pressure. However, if the p-value is higher than our level of significance, we would fail to reject the null hypothesis and conclude that there is insufficient evidence to support the claim that the new drug is effective in reducing blood pressure.

It is important to note that hypothesis testing is not a perfect method and is subject to sampling variability. The standard error is used to quantify this variability and is often used to calculate confidence intervals and determine the precision of estimates.

In conclusion, hypothesis testing is a powerful tool in statistical inference that allows us to make decisions based on data. It is essential for healthcare professionals to understand the basics of hypothesis testing, as it can inform clinical decision-making and help advance the field of medicine.

References:

1. Rosner, B. (2010). Fundamentals of Biostatistics. Boston, MA: Cengage Learning.