The odds ratio
measures the association or the relationship between a given attribute (A) and a second attribute (B) in a sample population. It specifically shows data analysts on how the absence or presence of attribute A affects the absence or presence of attribute B. You could use the odds ratio to determine how a certain exposure, (like lack of exercise for instance) affects a certain outcome (like diabetes) and to do a comparison of the risk factors for that particular outcome. The odds ratio could also be used to determine what quantity of processed foods, for example, lead to colon cancer. Or to find out if the usage of mobile phones is linked to brain cancer. In other words, as long as there are two properties that can be linked, one can always calculate the odds.
How is the odds ratio interpreted?
If the odds ratio
of a given experiment is exactly 1, then it means that the exposure to event A has no effect on the outcome of event B. However, if the odds ratio is higher than 1, it means that there is a higher likelihood of event B happening if exposed to event B. An odds ratio that is less than 1 indicates lower likelihoods of both events A and B happening.
But explaining the odds ratio is not quite as easy as described above. This is especially true when describing real-world situations. For instance, if you have a positive odds ratio, it does not always mean that you will have a positive result. You need to consider other factors such as p values and confidence intervals to effectively determine what the result will be. To understand the odds ratio in-depth and what it entails, we invite you to hire our odds ratio and relative risk online tutors.
is used to compare the likelihood or the probability of an event to occur between two groups. It is considered part of descriptive statistics (not inferential statistics
), as it doesn’t show statistical significance. This technique compares the likelihood of an event to occur in a group compared to the likelihood of an event to occur in another group. In other words, it requires data scientists to closely examine two dichotomous variables whereby one variable determines the outcome of an event (occurred versus not occurred) while the other measures determine the groups (group 1 versus group 2).
To calculate relative risk, you will divide the likelihood of an event to occur for group 1 by the likelihood of an event to occur for group 2. The relative risk is closely related to the odds ratio, only that the former is calculated using percentages while the latter is calculated using a defined ratio of odds. The values in a relative risk are always ≥ 0. If the value is 1, then the result is said to be neutral, meaning, the likelihood of an event to occur in one group is similar to the likelihood of an event to occur in the other group.
The application of the odds ratio and relative risk
The odds ratio
and relative risk techniques are commonly used in disease control. The odds ratio is particularly used in cases where the disease being investigated is rare. To obtain effective results from an odds ratio, both the cases and the controls should be representative of the population being investigated with respect to exposure.
Even though the odds ratio and relative risk are often used hand in hand, relative risk procedures are much easier to interpret and make more sense to readers. For instance, a relative risk of 9.0 simply means that the group that is affected is nine times at risk than the one that is not affected. Most people, (even those with very little knowledge of statistics) can grip this concept fairly easily. If you wish to have this topic elaborated further by a professional, feel free to connect with our odds ratio and relative risk homework help experts.