While everyone has heard of statistics, few people understand exactly what the subject entails and why it matters. An overview of the ins and outs of statistics is provided below. Its relevance to the financial field is explained.
What Are Statistics?
Let us start with a basic outline of what statistics are. The term comes from the Latin word ‘status’ and refers to a set of numerical figures that are utilized to provide a representation of information. The term ‘statistics’ denotes the technique that is used to collect data, which is then examined and analyzed so that the quantitative data that results can enable a person to arrive at conclusions. This supports management or governments in making the right decisions.
In other words, statistics are a means of systematically processing raw data so that they can be interpreted. The results are produced in a mathematical form that can be applied to decision-making or understanding information. This should be clear, simple, and comprehensive.
Various disciplines make use of statistics. For example, researchers in the psychology field will use statistics to compare two groups of participants when testing a hypothesis that a certain intervention will improve addictive behavior. One group will have the intervention applied to them, while the other group will be used as a control. Participants’ addictive behavior is tested with a self-administered survey before and after the intervention.
Apart from the example of how statistics are used for research in the social sciences, such as psychology, ethnology, and sociology, it is also applicable to businesses, the manufacturing sector, e-commerce, banking, finance, production, the IT field, human resources, medicine, mining, economics, robotics, machine learning, aerospace, and many more.
In the financial sector, data that is in a numerical format can be analyzed to determine how to proceed with assets, investments, and portfolios. Statistics can be used to provide forecasts for the weather and even sports performance. Likewise, the public sector and government utilize statistics to make decisions. Finally, it can also provide input for budgeting.
Understanding the Different Types of Statistics
There are two main types of statistics. These are descriptive and inferential statistics. As the name suggests, descriptive statistics are used to describe aspects of a given population. For example, if one in five babies are female and the rest are males, then twenty percent of all infants born over a certain period and country are female, while eighty percent are male. This can then be illustrated utilizing graphs and figures.
Descriptive statistics fall into two categories: measures of central tendency and measures of spread. Measures of central tendency are made up of the mode, median, and mean. The mean is the average of all the parts of a dataset, such as the ages of schoolteachers in Georgia who teach STEM subjects. The median is the middle figure. For example, if the ages of these teachers are 21, 25, 34, 47, and 52, then age 34 falls in the middle, whereas the mean was obtained by adding all the ages together and dividing them by the number of items in the set, in this case, five. So, the mean is 35.8. The mode shows the most common data in the specific sample. For example, if there are four students that are taking mathematics and their ages are 17, 18, 18, and 21, then the mode is 18.
Measures of spread are used to demonstrate the similarity and differences in a dataset. In the above example, the figures are fairly close together. Consider the data set 21, 25, 34, 47, and 52 that were used for the teachers above. One can easily see that the figures are quite widely spread out. Types of statistics that fall under measures of spread are range, quartiles, variances, absolute deviation, frequency distribution, and standard deviation.
Inferential statistics are used to draw inferences about a population by working with a sample from that population. The results are used to predict the characteristics of the population. There are, again, two types of inferential statistics: confidence intervals and hypothesis testing. An example of confidence intervals is,‘there is a 95% probability that a married man who has had an affair would have more than one affair’. Confidence intervals are usually 99%, 95%, and 90%. Hypothesis testing is used to analyze a sample of a population and use the results to state something about the greater population it belongs to.
Characteristics Of Statistics
A statistic should have certain properties. These are completeness, consistency, sufficiency, unbiasedness. Completeness of data means that all the data needed to analyze the information are available and that nothing is missing. Using the example of schoolteachers, if we do not have the ages of two of the teachers it will be difficult to make accurate interpretations from the data.
Consistency refers to the uniformity of the data or its stability. It can be measured using standard deviation, range, and variance. In this case, the standard error of the mean is commonly used. Consistency can also be measured by estimating how reliable the data set is when instruments are used to collect the data. An example of an instrument is an IQ test.
The sufficiency of a statistic is determined if there is no other statistic that can be calculated from the sample. In other words, no additional information is forthcoming by performing more calculations.
Unbiasedness of a statistic occurs if the mean of the sampling distribution and the expected value of the parameter are equal. The most basic example is the sample mean where the sample means is itself normally distributed with a mean equal to the population mean.
Statistics and Finance – How Are They Related?
Statistics are an important tool in finance. It is hard to work out the value of financial assets as markets become more complex. Statistics simplify this. Statistical analysis is used in the development of new financial products to determine the risk level. It has relevance for improved market valuation, in hedge funds, financial institutions, and investment banking. An example of statistics in finance is to determine what percentage of clients are likely to purchase a new financial product.
Statistics are relevant at both the macro and micro levels. A country measures its financial stability, economic growth, and interest rate projections at the macro level. At the micro-level, a company can determine earnings, income, and revenue generation. Financial forecasts and budget preparation also use statistics.
Many students take a course in statistics as part of their degrees. However, statistics have acquired such importance in all aspects of the modern world that more and more students are taking it as a major and using it as the basis for new careers that have opened up, such as the best careers for applied statistics majors.
The Importance of Accurate Statistics
For statistics to be meaningful and trustworthy, they must be of high quality. Decisions that feed into policies are made based on statistics. In a country, citizens will not be open to a new policy when the statistics on which it is based are unreliable.
It is also important that raw data are sufficiently detailed to generate true insights into a situation. Complex decisions cannot be made on the basis of simplistic data. The global financial crisis brought this into the spotlight and resulted in a call for more micro statistics instead of aggregates. This will help to identify problems at an early stage before they turn into a crisis. This means looking at individual transactions, such as loans, and not only at the overall status. This type of data is referred to as granular. It can ensure a better grasp of how a specific monetary policy will be transmitted to the various sectors of an economy. For example, its effects on manufacturing may be vastly different from its effects on banks.
It is necessary when assessing raw data that the confidentiality of the individual is protected. A report on loans would only extract the financial information and not the client’s name, address, and banking details. This is even more important when the data is shared between different parties.
Data harmonization is a key issue. When examining inflation data, for example, the raw data is taken from the same national database and involves the same set of goods and services. This allows for proper comparisons and interpretations. The same calculations are produced each year so that they are comparable year-on-year, enabling swift decisions to be made and trends to be revealed.
Such statistics should also be readily accessible for the private individual or business so that they can make their own decisions in their areas of activity. For example, if the price of cooking oil is set to rise dramatically, a household may want to look at alternatives to stick to their grocery budget.
Statistics are used in so many areas that it is worthwhile for any adult to have an understanding of the basics and how statistics are used to make important decisions for the country and at work, as well as how they can use statistics in their own decision-making.