There are many applications where it is of interest to compare two independent groups with respect to their mean scores on a continuous outcome. Here we compare means between groups, but rather than generating an estimate of the difference, we will test whether the observed difference (increase, decrease or difference) is statistically significant or not. Remember, that hypothesis testing gives an assessment of statistical significance, whereas estimation gives an estimate of effect and both are important.
CORRECTION: Scientists do strive to be unbiased as they consider different scientific ideas, but scientists are people too. They have different personal beliefs and goals and may favor different hypotheses for different reasons. Individual scientists may not be completely objective, but science can overcome this hurdle through the action of the scientific community, which scrutinizes scientific work and helps balance biases. To learn more, visit in our section on the social side of science.
aims and objectives – what’s the difference? | patter
: In everyday language, the word usually refers to an educated guess or an idea that we are quite uncertain about. Scientific hypotheses, however, are much more informed than any guess and are usually based on prior experience, scientific background knowledge, preliminary observations, and logic. In addition, hypotheses are often supported by many different lines of evidence in which case, scientists are more confident in them than they would be in any mere "guess." To further complicate matters, science textbooks frequently misuse the term in a slightly different way. They may ask students to make a about the outcome of an experiment (e.g., table salt will dissolve in water more quickly than rock salt will). This is simply a prediction or a guess (even if a well-informed one) about the outcome of an experiment. Scientific hypotheses, on the other hand, have explanatory power they are explanations for phenomena. The idea that table salt dissolves faster than rock salt is not very hypothesis-like because it is not very explanatory. A more scientific (i.e., more explanatory) hypothesis might be "The amount of surface area a substance has affects how quickly it can dissolve. More surface area means a faster rate of dissolution." This hypothesis has some explanatory power it gives us an idea of a particular phenomenon occurs and it is testable because it generates expectations about what we should observe in different situations. If the hypothesis is accurate, then we'd expect that, for example, sugar processed to a powder should dissolve more quickly than granular sugar. Students could examine rates of dissolution of many different substances in powdered, granular, and pellet form to further test the idea. The statement "Table salt will dissolve in water more quickly than rock salt" is not a hypothesis, but an expectation generated by a hypothesis. Textbooks and science labs can lead to confusions about the difference between a hypothesis and an expectation regarding the outcome of a scientific test. To learn more about scientific hypotheses, visit in our section on how science works.
What is the difference between objective and subjective …
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'(defined as H0). Along with this null hypothesis, an alternative hypothesis (H1) should be stated, which will relate to the statement made if 'sufficient' evidence against H0 is found. An example of a null and an alternative hypothesis is: H0:there is no association between the prevalence of disease and exposure to factor x; H1: there is an association between the prevalence of disease and exposure to factor x. However, in some occasions, the null and alternative hypotheses may have a direction - for example: H0: the prevalence of disease amongst animals exposed to factor x is not higher than amongst animals not exposed to factor x; H1: the prevalence of disease amongst animals exposed to factor x is higher than amongst animals not exposed to factor x. The differences in these relate to whether a will be used.
22.04.2010 · Research Objectives and Hypotheses
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). For the purposes of this page, the use of hypothesis testing in analytic studies will be focussed on. 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.