Introduction To Statistics: Definition, Benefits, Methods

Definition

Branch of mathematics concerned with collection, classification, analysis, and interpretation of numerical facts, for drawing inferences on the basis of their quantifiable likelihood (probability). Statistics can interpret aggregates of data too large to be intelligible by ordinary observation because such data (unlike individual quantities) tend to behave in regular, predictable manner. It is subdivided into descriptive statistics and inferential statistics. (What are statistics? definition and meaning, n.d.)

Statistics is a form of mathematical analysis that uses a given set of experimental data or real-life studies to characterize and summarize quantitative models. Statistical studies methods for collecting, reviewing, analyzing, and drawing conclusions from data. Some statistical measurements include mean, regression analysis, skewness, kurtosis, variance, and analysis of variance. (KENTON, 2019)

Statistics is a term used to summarize the process an analyst uses to characterize a data set. If the data set depends on a sample of a larger population, the analyst can formulate an explanation about the population based primarily on the statistical results of the sample. Statistical analysis involves the process of collecting and evaluating data and then aggregating the data into a mathematical form. (KENTON, 2019)

Key Characteristics

Statistics are Aggregate of Facts

Only those facts that can be studied in terms of time, place or frequency can be called statistics. Individual, single or unrelated numbers are not statistical because they cannot be studied with each other. (M, n.d.)

Statistics are Affected to a Marked Extent by Multiplicity, of Causes

It is not easy to study the effects of one factor simply by ignoring the effects of other factors. Here, we must consider the impact of all factors on this phenomenon separately and collectively, because the impact of factors may change with location, time or situation. (Mehta, n.d.)

Statistics for a Pre-determined Purpose

Investigators must have a purpose in advance before they can begin collecting work. Data collected without any purpose is useless. Suppose we want to know the intelligence of some people, we must not collect data on income, attitudes and interests. If there is no clear purpose, we will not be able to distinguish between necessary data and unnecessary data or related data and irrelevant data. (M, n.d.)

Benefits of Statistical Data for Meeting Business Objectives

Eases Performance Management and Evaluation

Managers can more easily assess performance and performance management to handle their roles better by using statistics in the business This is done by the manager collecting data on employee performance based on the number of completed responsibilities or the number of outputs delivered. Once the data is collected, managers can evaluate to identify areas where problems exist or areas that need improvement to maximize the use of the business. (Yiga, 2017)

Research and Development

Through different forms of statistical data, such as surveys and indiscriminate samples, business can gather the best information about which market is best to develop products or services. Research enables businesses to be practical even with consumer’s ever-changing demands and their behavior when product prices go high. (Yiga, 2017)

Projection of Future Events

With the use of statistics, it is very likely that you will anticipate possible future events, and it is always wise to be prepared for any risk factors that may arise in the future so that your business does not have much impact when it actually happens. In addition, it helps predicting what might happen in the future, statistics can help you make the perfect future plan or budget, which is a clear identity for a company that is interested in growing. (Yiga, 2017)

Sources and Types of Data and Information

Web and Printed Publications

Statistics are published on the web and in printed publications. Publishers include the federal government, state governments, foreign governments, international agencies, private entities, and membership organizations. The best strategy for finding statistics is to identify stakeholders (governments, companies, organizations) and thoroughly search their publications and Internet pages. (Company and Industry Statistics - Data and Statistical Sources: Labor and Employment, n.d.)

Surveys

Also, collecting data through surveys usually involves the original source obtained directly from the observation unit in order to generate statistics Using surveys to compile statistics can provide data compilers with important control over the quality of the statistical process and results. This traditional method of data collection is related to a large number of well-established statistical methods, from questionnaire design, sampling and collection techniques to data cleansing, to aggregate and estimate totals. (Data-sources-for-business-statistics.pdf)

Value of Statistical Method

Mean

The arithmetic mean, commonly referred to as the 'average', is the sum of the number list divided by the number of items in the list. (Dillard) The average can be used to determine the overall trend of the data set or to provide a quick snapshot of the data. Another advantage of the average is that it is very simple and fast to calculate. However, in a data set with a large number of bias distributions, the average does not provide the accuracy needed for careful decision making at all.

Median

The median is the value that occupies the middle position when all observations are sorted in ascending/descending order. It accurately divides the frequency distribution into two halves. 50% of the observed values in the distribution are equal to or lower than the median. Therefore, the median is the 50th percentile. (Central, 2011) It is easy to compute and comprehend and is not distorted by outlier’s data. Also, it can be determined for ratio, interval, and ordinal scale. However, median is unlike the mean, it does not apply to further mathematical calculations and is therefore not used for many statistical tests.

Mode

A pattern is defined as the most frequently occurring value in the data. Some data sets have no patterns because each value only appears once. On the other hand, some data sets can have multiple modes. This happens when the data set has two or more equal frequency values, which is greater than any other value. In addition to describing the bimodal distribution, patterns are rarely used as summary statistics. In the bimodal distribution, the higher peak is called the primary mode and the shorter peak is called the secondary mode. (Central, 2011) It is the only measure of central tendency that can be used for data measured in a nominal scale. However, it is not used in statistical analysis, as there are less samples. Thus, the fluctuation of the observation frequency is greater.

Standard Deviation

The standard deviation is a measure of data propagation around the mean. A high standard deviation indicates that the data spreads more widely from the mean, where a low standard deviation indicates that more data is aligned with the mean. (Dillard) In a combination of data analysis methods, standard deviation is useful for quickly determining the dispersion of data points. However, standard deviation is deceptive if used alone (without other methods).

Deductive and Inductive Approaches

When developing new theories, researchers can choose from two main methods which are deductive methods and inductive methods. (Andersson, 2008) The deductive method starts with theory. The researchers draw a hypothesis from existing theories and then collect empirical data to support this hypothesis. The main purpose of the deductive method is to test and explain the theories. (Andersson, 2008) In contrast, inductive methods are used to collect arguments and then shift to deduction, in order to complete the study and analyze the arguments with appropriate theories. Research begins with collecting data to generalize social life or specific behaviors for generalization. Hence, the general statement drawn from the generalization will aim to produce new theories. (Bailey, 1996)

Implication for Business Intelligence

Social media has brought about a revolution and has determined the paradigm shift in global company operating strategies. It leads to the collection of large amounts of data from various social media channels, so it must be used for business intelligence purposes. It has had a major impact on theory and practice to develop plans and strategies to optimize the benefits of social media channels for business value. (S. Prasanna Devi, 2016)

Example of Analysis

Netflix uses big data analytics for targeted advertising. The company has more than 100 million users and collects large amounts of data, which is key to achieving the status of the Netflix industry. Netflix realize that a typical Netflix member loses interest after perhaps 60 to 90 seconds of choosing video to watch. Therefore, if you are a subscriber, they will send you suggestions for the next movie you should watch. This is done using subscribers’ past searches and viewing data. The recommendation system influences about 80% of the content streamed on Netflix. (5 Real-World Examples of How Brands are Using Big Data Analytics, n.d.) It is used to give them insights about what the subscriber is most interested in and focusing on high-potential customers with the right products.

Netflix will find the popular series. It has spent $100 million on 26 episodes of House of Cards, as they were confident the show could be marketed successfully to their audience. This is because they knew it would appeal to the fans of the original British House of Cards and the built-in fan bases for director David Fincher and actor Kevin Spacey. (5 Real-World Examples of How Brands are Using Big Data Analytics, n.d.) Actually, Netflix has done the analysis by using big data. It collects the data from ratings, searches, do portions of programs get re-watched etc. And predict the trend of the movie. Finally, gather big data from a massive amount of behavioral data and uses the data to provide a better service for customers.

Suitability of Data Analysis

Quantitative Analysis

Quantitative research is used to quantify problems by generating digital data or data that can be converted into usable statistics. It is used to quantify attitudes, opinions, behaviors, and other defined variables and to summarize results from larger sample populations. Quantitative research uses measurable data to develop facts and reveal patterns in research. Quantitative data collection methods are more organized than qualitative data collection methods. Quantitative research is about numbers. It uses mathematical analysis and data to clarify important statistics about your business and market. Through strategies such as multiple-choice questionnaires, this type of data can help you measure the interest of the company and its products. As quantitative research is based on mathematics, it is statistically valid. This means you can use the findings to predict your business directi

Qualitative Analysis

Qualitative research is primarily exploratory research. It is used to understand the underlying causes, opinions and motivations. It provides insight into the problem or helps to present ideas or assumptions for potential quantitative research. Qualitative research is also used to reveal trends of ideas and perspectives and to delve the issues. Qualitative research is not about numbers, but about people, and how they think about your business. It is usually done by asking questions one-on-one or a group of people. Qualitative research can help you define problems and understand your opinions, values and beliefs. As qualitative research usually involves a smaller sample size than quantitative research, it is not aim to predict future performance. Instead, it provides a better image on the business.