Statistics is among the twenty fastest-growing degrees in the nation. Its application is extensive, and it currently holds a central position in numerous fields, including oil and gas, medical research, and finance. Applied statistics involves the implementation of specific procedures to obtain numerical information that is statistically known as data. Understanding how to present and interpret data is the foundation for evidence-based research. The demand for oil and gas is continuously rising. Worldwide, 100 million barrels of crude oil were consumed per day in 2018; however, the production of oil that same year was 90 million barrels per day. With production falling behind, petroleum enterprises are working hard to produce record amounts of oil and gas every day.
This is not an easy task because oil extraction can be unpredictable. The distribution of oil-and gas-bearing basins is scattered, and because they exist underground, it is impossible to tell whether hydrocarbon is present without drilling. This means that data have to be obtained through exploration first. This can be time-consuming and expensive, but once the data is obtained, researchers can use statistics to know exactly where to drill. This can be carried out by analyzing underground hydrocarbon resources to determine their existence, migration, and accumulation. One statistical method that can be implemented is Bayes’ theorem. It uses prior knowledge of conditions related to an event to predict its probability. In the oil and gas field, the analysis begins by first calculating the prior probability of finding oil underground. Then, that information is used to compute a conditional probability.
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Applying Bayes theorem, the conditional probability is reversed to obtain subsequent probabilities. Using the statistical method mentioned above, the investigator can estimate the possibilities of finding oil by inverting previous information. It also provides a way to revise existing predictions given new data. The oil market is becoming very competitive. Analyzing data can help petroleum enterprises obtain resources more productively. The search for hydrocarbon reserves is characterized by uncertainty and is inherently probabilistic. Without the use of statistics, it would be challenging to compete in the current oil market.
Statistics is also used in the medical field. One of the most popular areas of medical research is cancer research. Uncontrolled cell growth causes this disease, and new technologies allow vast information to be extracted from abnormally behaving cells. However, data are meaningless unless properly analyzed. Statistics provides a way for researchers to meaningfully and accurately conduct their studies.
There are two main types of clinical research: observational studies and clinical trials. Observational studies are focused on studying ongoing behavior, such as the effect of specific cancer treatments. In medical concepts, this is vital because it helps understand disease mechanisms. However, these tests are non-randomized and uncontrolled, thus vulnerable to confounding variables that may affect the results. Researchers usually identify potential sources of bias prior to the study, however regression based approaches are commonly used to reduce bias. One example is linear regression, it models the relationship between two variables by measuring the difference between predicted values and observed ones through the best fit line. The results can be plotted on a graph using modules such a Python.
On the other hand, randomization is one of the fundamental parts of clinical trials. Unlike observational studies, the variable of interest is manipulated, and the researchers have the ability to control confounding variables, thus reducing bias. Hypothesis testing methods, such as the Chi-square test are commonly used. The Chi-square test measures the relationship between two categorical variables and results in a p-value that tells us if our results are significant or not. An example could be the relationship between body size and breast cancer. However, these tests can only give us an idea of how well the treatments work. Research involving humans is subject to sudden changes. This may be due to genetics, the environment, or behavior.
Researchers compute confidence intervals at the end of the study to show how confident they are that the results did not happen by chance. In the medical field, where precision plays a critical role, statistics is an essential discipline. The panic of 1873, also known as the Long Depression, was the first global financial crisis. Before this time, national authorities did not know how to control their countries' money supply. This caused thousands to see their wages drop or even lose their jobs. The Long Depression raised awareness of the need to create statistical concepts to analyze and record data that could foresee financial problems. A ubiquitous area in finance is risk management. This is because investors are continually making decisions that could prove beneficial or detrimental.
The Monte Carlo simulation is one example of a statistical method that can be used. It relies on repeated random sampling to obtain numerical results. By attempting to replicate the uncertainty of the real world, the Monte Carlo simulation helps the investor see which investment alternative will have the highest probability of success. Another way to assess risk is by testing for normality. This is done by plotting the data in a normal curve and then analyzing it as it spreads from the mean.
The further a price action moves from the mean, the more likely an asset is being over or under-evaluated. The standard deviation is generally used to indicate how scattered the data is concerning the average —the greater the standard deviation, the greater the dispersion of the data, which is usually shown in a graph.
In every field, statistics-based research involves testing the effect of independent variables on dependent ones to prove or reject a hypothesis. Through tests, such as those mentioned in previous paragraphs, we can answer, for instance, if a parameter is equal to a specific value. The results are presented with a range of error and a degree of confidence. As described in each of the indicated disciplines, statistics deals with obtaining a sense of proportion that measures the probability and significance of the collected observations.
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