Changes

Null hypothesis testing (often described just as '''hypothesis testing''' is very commonly used in epidemiological investigations, and may be used in both analytic studies (for example, assessing whether disease experience differs between different exposure groups), and in descriptive studies (for example, if assessing whether disease experience differs from some suspected value). As in most studies, only a sample of individuals is taken, it is not possible to definitively state whether or not there is a difference between the two exposure groups. Hypothesis tests provide a method of assessing the '''strength of evidence''' in favour or against a true difference in the underlying population. However, despite their widespread use, the results of hypothesis tests are often misinterpreted.<br>

Null hypothesis testing (often described just as '''hypothesis testing''' is very commonly used in epidemiological investigations, and may be used in both analytic studies (for example, assessing whether disease experience differs between different exposure groups), and in descriptive studies (for example, if assessing whether disease experience differs from some suspected value). As in most studies, only a sample of individuals is taken, it is not possible to definitively state whether or not there is a difference between the two exposure groups. Hypothesis tests provide a method of assessing the '''strength of evidence''' in favour or against a true difference in the underlying population. However, despite their widespread use, the results of hypothesis tests are often misinterpreted.<br>

==Concept of null hypothesis tests==

==Concept behind null hypothesis testing==

Hypothesis tests provide a systematic, objective method of data analysis, but do not actually answer the main question of interest (which is commonly along the lines of 'is there a difference in the disease experience between individuals with this exposure and individuals without this exposure?'). Rather, hypothesis tests answer the question 'if there is no difference in disease experience between individuals with or without this exposure, what is the probability of obtaining the current data (or data more 'extreme' than this)?

Hypothesis tests provide a systematic, objective method of data analysis, but do not actually answer the main question of interest (which is commonly along the lines of 'is there a difference in disease experience between individuals with exposure x and individuals without exposure x?'). Rather, hypothesis tests answer the question ''''if there is no difference in disease experience''' between individuals with or without exposure x, what is the probability of obtaining the current data (or data more 'extreme' than this)?' As such, hypothesis tests do not inform us whether or not there is a difference, but instead they offer us varying degrees of '''evidence''' in support of or against a situation where there is no difference in the population under investigation. This situation of 'no difference' is known as the 'null hypothesis', and should be stated whenever a null hypothesis test is performed. If there is little evidence against the

==Hypothesis testing and study power==

==Hypothesis testing and study power==