Hipótesis 2 Acción de apoyo accidental sobre la carabina

At the start of this chapter, we definedbusiness statisticsas statistics used to

help with business decisions. In business, decisions are everywhere, little ones, big ones, trivial ones, important ones, and critical ones. As the quotation by Abraham Lincoln suggests, the more we know about what is going on, the more likely we are to make the right decision. In the ideal, if we knew specifics about the future outcome of our decision, we would never make a mistake. Until our boss buys us a crystal ball so that we can see into the future, we will have to rely on using information about the present.

QUICK QUOTE

If we could first know where we are, and whither we are tending, we could better judge what to do, and how to do it.

Abraham Lincoln

But what sort of information about the present will help us make our decision? Even if we know everything about what is going on right now, how do we apply that information to making our decision? The simple answer is

that we need to look at the outcomes of similar decisions made previously in similar circumstances. We cannot know the outcome of our present decision, but we can hope that the outcomes of similar decisions will be similar.

The central notion of all statistics is that similar past events can be used to predict future events. First and foremost, this assumption explains why we

have defined statistics as the use of numbers to describe generalfeatures of

the world. No specific fact will help us, except for the specific future outcome of our decision, and that is what we can’t know. In general, the more we know about similar decisions in the past and their results, the better we can predict the outcome of the present decision. The better we can predict the outcome of the present decision, the better we can choose among the alternative courses of action.

FUN FACTS

The statistical notion that past events can be used to predict future ones is derived from a deeper philosophical notion that the future will be like the past. This is a central notion to all of Western science. It gives rise to the very famous ‘‘Humean dilemma’’ named after the philosopher, David Hume, who was the first person to point out that we cannot have any evidence that the future will be like the past, except to note that the future has been like the past in the past. And that kind of logic is what philosophers call avicious circle.

We discuss this problem more deeply in Chapter 16 ‘‘Forecasting.’’

There are three things we need to know before statistics can be useful for a business decision. First, we need to be able to characterize the current decision we face precisely. If the decision is to go with an ad campaign that is either ‘‘edgy’’ or ‘‘dynamic,’’ we will need to know a lot about what is and is not an edgy or a dynamic ad campaign before we can determine what information about past decisions will be useful. If not, our intuition, unassisted by statistics, may be our best bet. It is also important to be able to determine what general features of the world will help us make our decision. Usually, in statistics, we specify what we need to know about the world, by framing a question about general characteristics of the world as precisely as possible. And, of course, we don’t need to describe the whole world. In fact, defining which part of the world we really need to know about is a key step in deciding how to use statistics to help with our decisions. For example, if we are predicting future sales, it is more valuable to know if our company’s specific market is growing than to know if the general economy is improving. We’ll look at these issues further in Part Four, when we discuss forecasting.

Second, there needs to be a history of similar situations that we can rely upon for guidance. Happily, here we are assisted by nature. Wildly different situations have important features in common that we can make use of in statistics. The important common elements can be found and described by abstracting away from the details of the situation, using numbers. This most important concept of abstraction is very simple and we have a lot of experience with it. We all learned very early on that, once we learned to count marbles and pencils we could also count sheep, cars, and dollars.

When we think about what we’ve done, we realize that we’ve defined a new practice, counting, and created a new tool for understanding the world, the count. The number of pennies in a jar or the number of sheep in a flock is not a specific fact about one specific penny or sheep. It is a general fact about the

contents of the jar or the size of the flock. Acountis a statistical measure that

we use to tell us the quantity we have of an item. It is the first and simplest of

what are called descriptive statistics, since it is a statistical measure used to

describe things.

If our general question about the world merely requires a description of the current situation or of previous similar situations as an answer, descriptive statistics may be enough. Examples of questions that call for descriptive statistics are:

. How many married women between 18 and 34 have purchased our

product in the past year?

. How many of our employees rate their work experience as very good

or excellent?

. Which vendor gave us the best price on our key component last

quarter?

. How many units failed quality checks today?

. How many consumers have enough disposable income to purchase our

premier product?

Third, there needs to be a history of similar decisions that we can rely

upon for guidance. While descriptive statistics have been around in some form since the beginning of civilization and the serious study of statistics has been around for almost a thousand years, it has been less than a hundred years since statisticians figured out how to describe entire decisions with numbers so that techniques useful in making one decision can be applied to other, similar decisions. The techniques used are at the heart of what is

calledinferential statistics, since they help us reason about, or make inferences

precisely phrased questions. In general, inferential statistics answers questions

aboutrelationsbetween general facts about the world. The answers are based

not only on relationships in the data, but also on how relationships of that same character can have an important effect on the consequences of our decisions.

If our question about the world requires a conclusion about a relationship as an answer, inferential statistics may be able to tell us, not only if the relationship is present in the data, but if that relationship is strong enough to give us confidence that our decision will work out. Examples of questions that call for inferential statistics are:

. Have men or women purchased more of our product in the past year?

. Do our employees rate their work experience more highly than do our

competitors’ employees?

. Did our lowest priced vendor give us enough of a price break on our

key component last quarter to impact profits?

. Did enough units fail quality checks today to justify a maintenance

call?

. How many consumers have enough disposable income to purchase our

premier product if we lower the price by a specific amount?

TIPS ON TERMS

Descriptive statistics. Statistical methods, measures, or techniques used to sum- marize groups of numbers.

Inferential statistics. Statistical methods, measures, or techniques used to make decisions based groups of numbers by providing answers to specific types of questions about them.

Using statistics to make decisions in business is both easier and harder than using statistics in the rest of life. It is easier because so much of a business situation is already described with numbers. Inventories, accounts, sales, taxes, and a multitude of other business facts have been described using numbers since ancient Sumeria, over 4000 years ago. It is harder because, in business, it is not always easy to say what makes the best decision best. We may want to increase profits, or market share, or saturation, or stock price, etc. As we will see in Part Four, it is much easier to use statistics to predict the immediate outcome of our decision than it is to know if, in the end, it will be good for business.

CASE STUDY

Selling to Men and Women

For example, say that we know that more women than men bought our product during the Christmas season. And we know that, statistically, more women between 18 and 34 bought our product than the competitors’. Does that tell us whether we should focus our advertising on men or women in the spring? Not necessarily. It depends on whether we are selling a women’s perfume or a power tool.

If perfume, maybe we should focus on men to buy Valentine’s Day gifts. Or maybe on women, so they’ll ask their husbands and boyfriends for our perfume by name.

If a power tool, then the Christmas season sales might be gifts. And a spring advertisement might be better focused on men who will be getting ready for summer do-it-yourself projects.

The lesson: Statistics may or may not be valuable to business. Common sense always is. If we use statistics, be sure to use them with some common sense thrown in.

CRITICAL CAUTION

Good statistics is not just a matter of knowing how to pick the techniques and apply them. Good statistics means knowing what makes for the best outcome and what the problems are in measuring the situation. Good business statistics demands a good understanding of business.