When talking about the basic fundamentals of business statistics, it is vital first to understand what statistics represents and its evolution to its current format where it can be used for business.
Statistics and Its Characteristics
The etymology of the word statistics comes from the Latin word status via the Italian word Statista. Similar to the English word, these words mean “State”, namely the geopolitical kind. Since the earliest form of data collection was about people and was used for administrative and political purposes, the name stuck. The current dictionary definition of the word refers to facts that can be numerically expressed and often tabulated. Based on this definition, there are specific characteristics that statistics follow.
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It is an aggregation of facts. It means that standalone figures cannot take any statistical form. We can neither compare them nor can any meaningful inference be drawn from them. Only an array of facts that allows us to reach a logical conclusion can be considered statistical data. All statistics involves numerical data, but not all numbers constitute statistics.
Another essential characteristic is the numerical representation of statistics. If the data cannot be numerically expressed, it is not statistics. Objective facts, therefore, often qualify, whereas subjective attributes like greed, honesty, corruption, to name a few, can only be expressed as a percentage of people having an opinion of that feeling.
Furthermore, statistics should be mutually related. It means that the data within a statistical set can be compared with one another. If the data is unrelated, then we cannot call it statistics.
Statistics should be methodically collected for a fixed purpose. Before collecting the data, statistics requires that the plan of work be defined along with the scope. Statisticians should adhere to these. Along with this, there must be a pre-defined purpose for beginning the process. Without specifying any of them, we cannot gather statistical data.
Statistics are variable and reactive. The numerical data collected is often not independent and depends on various outside factors and variables, which influence them.
The final characteristic of statistics is that there should be a reasonable expectation of accuracy in the collection of statistics. Statistics, by definition, deals with a large aggregate of data. Therefore, when drawing inferences, it is often not possible to deal with the entire data set. At such times, the process statisticians use is called sampling. They take a sample and then draw their conclusions from it. They then apply it to the real data set that the selection is supposed to represent. The accuracy of the sample is dependent on the type of enquiry and its objective. If this accuracy is not maintained, extrapolation to the entire data might give faulty results.
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What does Business Statistics Mean?
A very recent survey in an investment news source reported that nearly all businesses face a need to manage unstructured data. Almost half of the respondents said that they need that done frequently. These statistics point to the need of business statistics in the workplace. Organizations are nowadays increasingly dependent on professionals who can take statistical data and analyze them to draw logical inferences for growth in business productivity. In today’s cut-throat business environment, using and not using business statistics can be the difference between growth, competitiveness, financial stability, and growing bust. Professionals who are qualified in this field interpret the business’s data, and they can give suggestions to the management to enhance health and growth in the company.
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When we define business statistics, it means the methods used to analyze, demarcate, interpret and compile data. Personnel having the requisite qualifications or experience in business statistics can reach conclusions from these numerical arrays on various parameters. They can include products, services, target audience, competitors, and consumers, which can all help the business reach the right decisions to grow their business. Statisticians and analysts can create multiple models from the same data set to observe trendlines and projections.
One of the major reasons to interpret the statistical data is to know about the company’s current health and not just the future projections. Understanding the current health of the company can help create better policies in real-time. On the other hand, they can use statistics to infer the possible future growth of the business. This concept is known as inferential statistics.
By studying various statistical models, business statistics personnel in a company can distinguish between the different supply and demand trends currently roiling the market and help the company adjust to the situation. Experts who can identify the meaning from such statistical data are often in a position to give a competitive edge to their own companies by allowing them to adjust their strategies in real-world scenarios. One of the most important components of such data is to lead long-term thinking in an organization. This allows to establish business targets, which in turn dictate the Key Performance Indicators (KPI) of all employees in that company.
Business Statistics Concepts
In business statistics, as with the larger field, there are two streams within it. One of them is descriptive, the other is inferential. Descriptive statistics work by taking into account various central tendency measures and calculating variabilities, spread, and deviations from there. On the other hand, inferential statistics deal with drawing an inference from a data sample by extrapolating it to the whole data set. It involves complex mathematical functions and business professionals generally use it.
Detailing more, descriptive statistics uses its interpretation on a finite data set with a fixed number of elements. Professionals in this realm use this discipline of statistics when analyzing one distinctive data set. Experts can use this data set to gauge an idea of mean, median and mode. From taking this information, they can then establish patterns about the whole population. This helps an organization evaluate present data trends, such as an average volume of subscriptions per month and compare it to the cost of generating content.
Inferential statistics, on the contrary, deals with projections from the current data set. It examines the connection between the sampled data set and the wider population. As mentioned before, it is not feasible to often study the entire population. Inferential statisticians send out, for example, feedback surveys to a subset from the total set of clients, who they feel are representative. Based on the sample size’s feedback and analysis, inferences can be drawn about potential feedback from all clients.
Those professionals in this line must have a firm grasp of measurement concepts. Various such concepts relate to both descriptive and inferential statistics. One such concept is sampling distribution, which refers to the probability distribution within a statistical data set by considering many samples within the same population. This helps experts to earmark multiple random samples of a population. An example of this concept in action can be found in the probability of two different customers of different ages buying the same product at different intervals. It can help derive the time distribution without having to ask physically or by email. Inferential statistics also employs sampling distribution to derive information about a population. Another such measurement concept is the normal random variable.
Business Statistics and Its Value
Having learned some of the basic fundamentals of business statistics,we now have to understand its value to the corporation. In other words, why does an organization need qualified professionals in this field at all? The straightforward answer to this question is that we can streamline operations in the present and profitability in the future with business statistics. By aligning KPIs, it can also ensure that human resources work to maximum efficiency.
On the subject of efficient human resource management, professionals can use business statistics internally to increase employee productivity. For example, statistical data shows that a particular employee struggles to meet targets regularly at the end of the week. Still, at the beginning and in the middle, they are instead ahead of the curve. Statisticians can draw such inferences from the data and report to the management. In turn, the management might decide that instead of setting targets for that particular employees right before the weekend, a better option might be to set it in the middle of the week.
Another direct value of business statistic is that it can be used to make the company’s financial position more secure in the market. This often requires business statisticians and analysts to work together with the accounting team. They can use statistical methods to note possible profit streams and they can use the same techniques to create budgets regularly to ensure that the company remains financially sustainable in the short-term. These experts can also discern various trends in the market, especially at it relates to consumer habits. Based on annual data, they can project the product demand based on a particular time.
Nowadays, business statistics have entirely changed the way organizations collect and interpret data. Most organizations, cutting across industries, now lean on data to analyze trends they then use to make vital decisions for internal business and external market-related issues.
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