Management Essay Assignment on Using Statistics in Business Decisions

Using Statistics in Business Decisions

Data Collection

Businesses and organizations of all types produce significant quantities of data. The majority of these data are generated from normal business operations. For example, each sales transaction of a beverage manufacturer will contain data about the date of the sale, the product sold, the amount of the sale, the customer, the region of the sale, and perhaps the salesperson. These are primary data, and collection of this type of data typically occurs automatically within the organization through the organization’s databases that store the transactions. Other primary data are collected to measure operational performance toward specific objectives. For example, a package delivery company may track driver safety performance over a period of time over certain routes during each day of the week. A copper mining company may collect information on the weight of ore hauled by each truck to understand if truckloads are optimized. A retail store may collect data on customer traffic in the store each hour. Credit card companies may measure how quickly customer service calls are answered.

In addition to the company and transaction-specific data, organizations and businesses frequently collect financial and market data about competitors, suppliers, customers, and others to guide effective decision-making. This data collection effort occurs outside the business and might involve tools such as surveys, interviews, and questionnaires. For example, a manufacturer of appliances might ask its suppliers to produce data on financial health, safety records, and measures of product quality. A consumer goods company may distribute a new product in a defined region and follow up with telephone interviews to assess the product’s performance and attractiveness. National Family Opinion is a national organization that uses the Internet and mail surveys to understand consumer preferences and reactions to specific grocery and nongrocery items. Other sites collect data on political opinion for use by political campaigns and journalists. A financial services company that is interested in acquiring another financial institution would require data on the acquisition target’s customer base, branches, and deposits. If any of these data are collected by a third party, such as an independent market research group, or if the data are purchased (such as a customer list), the data are considered secondary data. Whether primary or secondary data, users must always be aware of the collection tools and data sources to ensure fairly measured and accurate data. Having correctly measured data from unbiased samples or sources is critical to correct decision-making.

Graphs, Charts, and Tables

Graphs, charts, and tables are tools to help organize and aggregate data upon collection. Each tool helps transform the data into information useful in decision making. Graphs and charts visually present summaries and relationships. For example, a pie chart could visually summarize the percentage of sales dollars in each sales region for a beverage distributor. A frequency distribution could identify the number of salespeople that achieved specific performance targets (e.g., sales or sales growth). A bar chart might compare the quantity of ore hauled by different types of trucks in dry versus wet weather to estimate the effect of weather on mining production. Line charts or graphs frequently involve how performance changes over time. For example, a financial services company might plot a line chart of the growth in deposits by type overtime for its acquisition target. Another graph might identify a relationship between the change in sales of soft drinks versus bottled water over the last 5 years, or the change in sales around different levels of marketing. Unlike the visual relationships presented in charts and graphs, tables present summary data in columns and rows, leaving the user to identify and understand relationships. Presentation of data in tables allows users to better understand the data points, range, and distribution characteristics. What is the preferred method of presenting collected data? A combination of visual charts and graphs and tabular presentations ideally describe a set of data and explain the relationships. Pictures minimize the need to explain relationships verbally. However, tabular presentations of data allow users to develop their own understanding of the data and relationships.

Numerical Measures

Numerical measures describe data samples and populations to better understand the central point and spread of the data. Numerical measures, such as the mean, median, and mode describe the central tendency or common point of the data. Variance and standard deviation describe the spread of the data. The variance is the square of the standard deviation. Since the unit measure is squared in the variance, the standard deviation is more commonly used to get to the original units.

These measures together allow users to make inferences about the data that can be used to answer questions and make decisions with a statistical measurement of confidence. For example, consider a set of data collected to understand the effect of a smoking ban on restaurant sales. The mean sales are the average level of sales. The median sales is the sales point where half of all observations are greater than the median and half are below the median. The most common data point defines the mode. Typically, the mode is more commonly reported among discrete or categorical data rather than continuous data. For example, if restaurant patrons rate service quality on a scale of 1 (poor) to 5 (excellent) with the following distribution, then the mode is 4.


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Although data may have similar central points, the variance and standard deviation describe how to spread out the data distribution is. For example, do a significant number of data points remain around the central tendency points or are the data points spread out on one or both sides of the center point? For example, although average restaurant sales are the same before and after a smoking ban, the level of the sales may be found to be more spread out prior to the ban compared to after the ban. Rather than concluding no effect of a ban, a difference in the variance of sales may suggest that a greater variety of customers are attracted to the restaurants after the ban.


M.U.S.E(2020). Using Statistics in Business Decisions.