Posts Tagged ‘extrapolation’

Radio Commercial Statistic: Another Example of Lies, Damn Lies, and then Statistics

May 10, 2010

Each morning, I awake to my favorite radio station, and the last few days, I’ve awakened to a commercial about a teaming up of Feeding America and the reality show Biggest Loser to support food banks.  While I think that’s a laudable joint venture, I have been somewhat puzzled by, if not leery of, a claim made in the commercial: that “49 million Americans struggled to put food on the table.”  Forty-nine million?  That’s one out of every six Americans! 

Lots of questions popped into my head: Where did this number come from?  How was it determined?  How did the study define “struggling?”  Why were the respondents struggling?  How did the researcher define the implied “enough food?”  What was the length of time these 49 million people went “struggling” for enough food?  And most importantly, what was the motive behind the study?

The Biggest Loser/Feeding America commercial is a good reminder of why we should never take numbers or statistics at face value.  Several things are fishy here.  Does “enough food” mean the standard daily calorie intake (which, incidentally, is another statistic)?  Or, given that two-thirds of Americans are either overweight or obese (another statistic I have trouble believing), is “enough food” defined as the average number of calories a person actually eats each day?

I also want to know how the people who conducted the study came up with 49 million people.  Surely they could not have surveyed so many people.  Most likely, they needed to survey a sample of people, and then make statistical estimations – extrapolations – based on the size of the population.  In order to do that, the sample needed to be selected randomly: that is, every American had to have an equal chance of being selected for the survey.  That’s the only way we could be sure the results are representative of the entire population.

Next, who and how many completed the survey?  The issue of hunger is political in nature, and hence is likely to be very polarizing.  Generally, people who respond to surveys based on such political issues have a vested interest in the subject matter.  This introduces sample bias.  Also, having an adequate sample size (neither too small nor too large) is important.  There’s no way to know if the study that came up with the “49 million” statistic accounted for these issues.

We also don’t know how long a time these 49 million had to struggle in order to be counted?  Was it just any one time during a certain year, or did it have to go for at least two consecutive weeks before it could be contacted?  We’re not told.

As you can see, the commercial’s claim of 49 million “struggling to put food on the table” just doesn’t jive with me.  Whenever you must rely on statistics, you must remember to:

  1. Consider the source of the statistic and its purpose in conducting the research;
  2. Ask how the sample was selected and the study executed, and how many responded;
  3. Understand the researcher’s definition of the variables being measured;
  4. Not look at just the survey’s margin of error, but also at the confidence level and the diversity within the population being sampled. 

The Feeding America/Biggest Loser team-up is great, but that radio claim is a sobering example of how statistics can mislead as well as inform.


Objective and Subjective Forecasting Approaches

May 3, 2010

(second in a series)

Today we discuss the various categories of forecasting methods that are available to businesses.  Forecasting methods can be either objective (using quantitative approaches) or subjective (using more intuitive or qualitative approaches), depending on what data is available and the distance into the future for which a forecast is desired.  Forecasting approaches will typically be more objective for nearer term forecasting horizons and for events where there is plenty of quantitative data available.  More distant time periods, or events with a lack of historical quantitative data will often call for more subjective approaches.  We will discuss these two classes of forecasting methods, and the categories within each.

Objective Forecasting Approaches  

Objective forecasting approaches are quantitative in nature and lend themselves well to an abundance of data.  There are three categories of objective forecasting methods: time series, causal/econometric,  and artificial intelligence.  AI approaches are outside my experience, so I won’t be covering them in this series, but mention them as another alternative, in case you wish to investigate them on your own. 

Time Series Methods

Time series methods attempt to estimate future outcomes on the basis of historical data.  In many cases, prior sales of a product can be a good predictor of upcoming sales because of prior period marketing efforts, repeat business, brand awareness, and other factors.  When an analyst employs time series methods, he/she is assuming that the future will continue to look like the past.  In rapidly changing industries or environments, time series forecasts are not ideal, and may be useless.

Because time series data are historical, they exhibit four components that emerge over time: trend, seasonal, cyclical, and random (or irregular).  Before any forecasting is done on time series data, the data must be adjusted for each of these components.  Decomposing time series data will be discussed later in this series. 

The most common time series methods include moving average (both straight and weighted), exponential smoothing, and regression analysis.  Each of these approaches will be discussed later in the series.

Causal/Econometric Methods

Causal or econometric forecasting methods attempt to predict outcomes based on changes in factors that are known – or believed – to impact those outcomes.  For example, temperature may be used to forecast sales of ice cream; advertising expenditures may be used to predict sales; or the unemployment rate might be used to forecast the incidence of crime in a neighborhood.  It is important to note, however, that just because a model finds two events that are correlated (e.g., occur together), it does not necessarily mean that one event has caused the other.

Regression analysis also falls under the causal/econometric umbrella, as it can be used to predict an outcome based on changes in other factors (e.g., SAT score may be used to measure likelihood of being accepted to a college).  Econometric forecasting methods include  Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models.  ARIMA was previously known as Box-Jenkins.  ARMA and ARIMA models are used in certain cases, but most of the time are unnecessary.  Although these two methods won’t be covered in much depth later in the series, there will be a brief description of them and when they are needed.

Subjective Forecasting Approaches

Subjective forecasts are more qualitative.  These approaches rely most heavily on judgment and educated guesses, since there is little data available for forecasting.  This is especially the case in long-range forecasting.  It’s easy to forecast next week’s sales of ice cream – and possibly even of individual flavors, since you’ll likely have months or years of past weekly ice cream sales data.  However, if you’re trying to get an idea of what ice cream consumption or flavor preferences will be 10 years from now, quantitative approaches will be of little use.  Changes in tastes, technology, and political, economic, and social factors occur and can dramatically alter the course of trends.  Hence, the opinion of subject matter experts is often called upon.  There is essentially only one category of subjective forecast approaches – and it is rightly called “Judgmental” forecasts.

Judgmental Methods

Judgmental forecasting methods rely much on expert opinion and educated guesses.  But just because they have little quantitative or objective basis doesn’t mean they should be dismissed or not measured for accuracy.  The most common types of of judgmental forecasting methods are composite forecasts, extrapolation, surveys, Delphi method, scenario writing, and simulation.  Each of these methods will be discussed in detail later in the series. 

Introducing “Forecast Fridays” – ON THURSDAYS!!!

Beginning with part 3, which will discuss moving average forecasts, the forecasting series will begin posting weekly so that the remaining days of the week can still be devoted to other topics in the marketing research and analytics field.  The weekly post will be called “Forecast Friday.”  However, it will be posted every Thursday!  Why?  Find out in tomorrow’s post!