Development↦Statistic Methodology↦Statistics in the Protocol↦The Hypothesis
What is it? Why is it important?
A statistical hypothesis is an assumption about a population parameter that is subject to empirical testing.
Example: a study assesses the effect of a new medication on blood pressure. The parameter in question is the mean blood pressure of the population.
Researchers establish a statistical hypothesis by formulating two competing hypotheses:
- A null hypothesis - also referred to as H0: represents a default assumption (i.e. no treatment effect exists. Based on the example above, no difference in blood pressure between the intervention and control group is detected)
- An alternative hypothesis - also referred to as Ha: suggests a difference or an effect (i.e. a treatment effect does exist. Based on the example above, blood pressure is higher in participants taking a placebo than in participants taking the medication under investigation)
Statistical analyses are used to assess the data collected in a study, enabling researchers to conclude whether H0 may or may not be rejected in favour of Ha.
What do I need to do?
As a SP-INV, think about the statistical framework of your study. As described in the sample size calculation topic, there are two possible frameworks:
- The precision-based framework
- The hypothesis testing framework
If the study aims at estimating a quantity with a certain accuracy, you are in the precision-based framework and therefore do not need to define a statistical hypothesis. Example: a study aims at estimating the proportion of deaths within 30 days of a myocardial infarction.
If you need to conduct a statistical test to address your research question, you are in the hypothesis testing framework, and need to define H0 and Ha.
Example: a study aims at testing the effect of an intervention (e.g. blood pressure lowering drug) between a placebo and intervention group.
Where can I get help?
Your local CTU↧ can support you with experienced staff regarding this topic
Basel, Departement Klinische Forschung, CTU, dkf.unibas.ch
Lugano, Clinical Trials Unit, CTU-EOC, www.ctueoc.ch
Bern, Clinical Trials Unit, CTU, www.ctu.unibe.ch
Geneva, Clinical Research Center, CRC, crc.hug.ch
Lausanne, Clinical Research Center, CRC, www.chuv.ch
St. Gallen, Clinical Trials Unit, CTU, www.kssg.ch
Zürich, Clinical Trials Center, CTC, www.usz.ch
References
ICH Topic E9 – see in particular
- 5.5 Estimation, confidence intervals and hypothesis testing