VARIOUS DATA-INFERENCE-PROPHECY ISSUES

DIP Issues

Though DIP is a rather simple process, many of its implications and results are complex and far-reaching for ourselves and others. To explore some of the issues surrounding DIP, we'll look at a number of examples. First, let's visit our friend Fred.

Fred is a salesperson for a large shoe company. At the beginning of the year he was told by his supervisor that his quota would be 240 units. It is October 15th and Fred is reviewing his year-to-date sales. He notices that his total sales for nine and one half months is 171 units. Fred has to make sense out of the data he has and he needs to know what might happen to him next. At the current rate of sales,18 units per month, Fred thinks to himself, "I'll never make my quota and if I don't make my quota, my supervisor will think that I'm a failure. I couldn't argue with that, I didn't do what I said I would do. Chances are he'll fire me and I'll be out looking for a job by January. I think if I start looking for a job now, I won't be without a job when I'm let go. I'll keep selling shoes, but I better spend as much time in my job search."

What are the data Fred has?

He talked with his supervisor at the beginning of the year.

240 unit quota for the year.

Nine and a half months have passed.

He has sold 171 units.

Look back at the features of data and see if the data laid out above meet the criteria. Did he or did he not talk to his supervisor? Yes. 240 units, is that either/or? Yes, either it is 240 or it is not. 240 isn't 239 or 241, is it? Have nine and one half months past? Yes, by the way we measure time, October 15th denotes that 9.5 months have gone by. What about 171 units? Well, that's how many he sold. He didn't sell any more or less.

These are the most significant data Fred has focused upon. There may be other data, but we are not aware of them. Given this data, Fred runs the data through his perceptual filters. His filters include the basic assumption "people get what they deserve" and the generalized expectancy that suggests "when people are presented with negative information, they react negatively." As his data moves to his inference level in this case, they are colored negatively.

Fred has many questions he has got to answer for himself. Below is the ultimate question Fred has asked himself and the answer he has derived from the data:

"I should be selling 20 units per month and I am only selling 18 on the average. What is a person who doesn't meet his goal? He is a failure, therefore I am a failure."

"I am a failure" is Fred's inference about himself. He has weighed the evidence (data) and judged himself to be a failure (inference). Afterall, the data support this conclusion, right?

Fred then predicts what will happen to him in light of the inference he has made (prophecy). He makes the prophecy that he will be seen as a failure by his supervisor and his supervisor will react negatively to his inability to make his quota. He further decides that failures shouldn't be allowed to continue working for any company and that he would be better off looking for a job. Do you think Fred will look for a job with another shoe company as a salesperson? Do you think Fred will look for a job in sales at all? Maybe. Maybe not.

This is a very simple example of how we may use DIP. This case didn't turn out too well for Fred. This one example demonstrates a couple of issues which involve how we use DIP and the possible affects the process can have on us and others.

"Is" is Trouble

To me, the little verb "is" causes more problems than any other word in the English language. When I know what something 'is', I have defined that item or object or place or person in no uncertain terms. On the inference level, we are compelled to identify and define the data we receive. In doing so, we very frequently resort to the word 'is' in the definition. Inferences are notorious for this.

The trouble starts with the meaning of the word 'is'. When we use 'is', we define the object of our attention in terms of equality or equivalence. When Fred inferred, "Fred is a failure," he was in effect saying, "The sum total of Fred equals failure." Obviously, the sum total of Fred does not equal failure. Fred 'is' much more than a set of events which at the time are negatively tinted.

Using 'is' in defining or labeling ourselves and others is quite natural. After we know what someone 'is', then we assume we know how to deal with him. We act as if we know everything there is to know about that person. We use the label as a time-saving and effort-conserving device. We do not have to work as hard at gathering data and understanding the other person when we know what he 'is.'

Pretend that you and I have been given a project to do. Our supervisor hands us a list of the people who have be assigned to our project team. She tells us, "These are the people we have available for you. Here's a brief run down on each of them. Sue is crazy. Ted is stupid. Frank is smart. Jerry is talented. Mary is pushy. Gary is weak. Harry is a wimp. Teresa is sweet. Darryl is crude. Pearl is a hick. Good luck on your project."

With this small bit of information, we start planning how we might use our human resources. What follows are some conclusions that we could draw.

Crazy people act crazy, so we can expect Sue to act crazy too. Let's keep an eye on her.

What about Ted? Well, Ted is stupid. Don't give Ted anything that requires any intelligence. Be sure you give him an easy job.

On the other hand, Frank is smart. Let Frank do the jobs that will require brain power.

As for Jerry, he is emotional. Let's not put Jerry in tough situations. He could breakdown and cry.

I bet we can put pushy Mary in charge and wouldn't have to worry if things would get done. People might not be happy with her, but things will get done.

Then there is Gary, the weak guy. If we need something that requires strength, we'd better find someone other than Gary.

I'm glad we put Mary in charge. If we had put Harry at the helm, people would have walked all over him.

We shouldn't forget Teresa. She's so sweet. I don't know if she'll be very helpful around here, but she makes everyone feel so good by just being here.

Then we have Darryl, the crude guy. He is a disruptive influence on the place. His obnoxiousness gets under everyone's skin. Perhaps we should ignore him for a while, and if he doesn't change his tune, we'll dump him.

Finally, we have Pearl to consider. Being the hick that Pearl is, we should probably send her to charm school before we turn her loose on the customers.

This situation may seem a little ridiculous and simplistic, but I assure you, it is what we do. We may be a little more sophisticated in our inferences and prophecies, we may not be as judgemental and reserve evaluations after some time has passed, but we reach conclusions about people based on what each person 'is' with lightning speed.

We must admit that when we know what someone 'is', we feel as if we have all we need to know to contend with him or her. We know whether or not we will have to protect and defend our sense of self. We can predict what onion skins would work best if we ran into these persons.

When you hear someone say, "He is ...", stop and think before you jump. 'Is' is a tell-tale sign of an inference. Remember that inferences are not good or bad, right or wrong. They are only an interpretation of some data. They are also insufficient in describing all the qualities of ourselves and others.

Labeling and Psychological Instruments

Another example when 'is' is used comes from psychological exams and personal style surveys. I've mentioned this issue in an earlier chapter, but I must comment again. Personality style surveys and behavior questionnaires are useful, but only to a point. As points of reference and griss for the conversation mill, they are helpful. However, many times they are misused.

For instance, I have worked with companies that use these instruments in their employment processes. Their personnel departments, more fashionably called the Human Resources Departments, administer the examinations to prospective employees. The tabulated results lead the givers of the exams to summary labels. If you answered so many questions a particular style with a secondary style of this or that. Each label often has an elaborate explanation of what this sort of person 'is' like, what an employer might expect from him, the strengths and weaknesses he might exhibit, and the problems and opportunities he could offer the company.

The number of possible labels that can be tagged on a person depends on the kind of instrument used. I've seen instruments that divide people into two or four groups. Other questionnaires have as many as 20 personality or behavioral styles. Of course, many of these examinations are tested for their validity and reliability. Validity means that the instrument measures what intends to measure. Reliability means the instrument measure the same the same way all the time. The problem I have with these instruments is they are often used improperly by barely trained people. These less than knowledgeable people use the labels to define the person they are considering and to act on this limited information.

Imagine you are applying for the job of Trust-Builder in my company. I ask you to take a personal style survey. After you take it, I tally your scores, and lrefer to my little booklet that tells me what the scores mean. Your score falls into the range of scores which suggest that your personal style can be describe as Suspicious. Given all the validity and reliability tests to which the survey was subjected, suspicious people fail at the job of Trust- Builder 85 % of the time. For my purposes, you appear not to be suited for the job. You are expecting me to be in touch with you and I must decide what I should do about you.

If I zero-in on the small piece of data generated in an hour and a half and decide that you 'are' that kind of person, I have summarized your existence by saying, "This person is suspicious." Whether it is fair or not, doesn't matter. It simply isn't effective on an interpersonal level to rely on simplistic labels.

We can all appreciate the fact that when a company is faced with tens, hundreds, and even thousands of job seekers knocking on the door at one time, the company does not feel it has the time, money, or energy to delve into each person individually. I can accept that. Companies have a job to do and they are trying to get it done at the least cost.

My point is simple. We must be very careful to summarize people with whom we must work and play with labels without recognizing that our labels are always insufficient. In addition, simply because a person has taken a particular instrument and has been found to be this way or that as opposed to any other way, does not mean we know everything we might need to know about that person.

Selective Perception

One job I had in my checkered past was credit manager of a bank's bankcard division. It was my job to say yea or nay to people who had applied for credit cards. We had an elaborate credit policy that set the boundaries of who deserved cards and who did not. Among the major considerations were length of employment, income, past credit history, time at current address, outstanding bills, and current monthly payments. In terms of DIP, these items were the data from which I inferred a person's credit worthiness and made a prophecy regarding the person's ability and willingness to pay their bills.

Also operating in my review of an applicant's data were perceptual filters I had developed regarding credit. I was a collector just prior to becoming a credit manager. I collected past due credit card accounts everyday for two years. Though I was and am a relatively trusting human being, from this experience my perceptual filters had developed a healthy skeptical and suspicious tint which prevented me from recklessly giving away our depositors' money.

I'll never forget the time I refused to grant a credit card to a particular young man, I'll call him Lester. The data he supplied on his application met all of our criteria and was verified with the references he supplied. Everything seemed fine except one item on his credit report. His credit record showed he had not paid a department store bill for months and the store had filed a law suit against him. This piece of data compared to our criteria compelled me to deny him an account until the issue was cleared from his credit report. Among the hundreds of refusal letters we sent out each week was one to Lester describing why he had been turned down.

The Postal Service was unusually fast because the next morning Lester called me to discuss why I had turned down his request for a card. I explained our policy, suggested a number of alternatives to handle the judgement, and even gave him a list of credit card companies which specialize in credit to people who had credit problems. He wanted nothing to do with my explanations, suggestions and alternatives. He wanted a credit card. I spent nearly three hours on the phone with him with no results. He finally decided to visit the office and deal with me face to face. I still didn't give him the credit card he thought he deserved. Lester, Sr. called me in a fury, wrote me a scathing letter, and went to the top of the bank to give the president a hard time. Eventually, the president of the bank asked me to issue the card just to get the guy off of our backs. I gritted my teeth and fumed at knuckling under to this pressure as I issued Lester an account.

Now I was mad. As his card went off in the mail, I went to the collections department and asked the manager to flag his account. I told her to watch his account closely and let me know if it ever went past due or over its limit. You see, in credit card agreements there was a provision that said the credit card was the property of the bank and use of it could be denied if the account was 30 days past due or over its limit for more than 30 days. If Lester could be nailed, I was going to nail him.

Months past, but the trauma of being challenged and overruled still festered in my craw. I went about my work, but every once in awhile I'd check on his account. Everything looked great. Lester was making me look like a fool. More months passed. Lester made his payments and stayed under his limit. A year and a half later the glorious day came. The collection manager left me a note saying she needed to talk to me about the Lester account. Lo and behold, Lester was over his limit. I asked her to keep me informed for the next 30 days. Each day seemed longer than the previous. I had a chance to get my revenge and I hoped Lester would come through for me.

The day arrived and no payment was received. I notified the authorizations department to disallow any more charges on Lester's account and if he attempted to use the card, have the merchant keep the card, cut it up, and mail it to me. The following week I found Lester's card on my desk. I fully expected to hear from the victim of my revenge, but I never did. His account was paid in full within days and that was the end of that.

As I recount this story, I feel a good bit of guilt. I am ashamed of myself. Sure, Lester was obnoxious and overbearing and I let him get to me, but I look back on the situation and recognize my unprofessionalism. What I did back then, and have done since, is no less than what all of us do at one time or another. I picked out the data I wanted and ignored all the rest. The fancy name for this is selective perception.

Selective perception means focusing on a limited amount of data to support our inferences about someone while rejecting all other available data that conflicts with or could refute our inferences. In Lester's case, I was on the prowl for any excuse (data) I could use to confirm my inference that Lester was bad news. I knew that customers went over their credit limits from time to time. I knew that we didn't yank everyone's credit card each time they exceeded their limits. And I knew I had acted without goodwill and good faith even though I had acted legally.

Dr. Timmons often said in class, "Do you know what the sweetest words one could ever hear? Not 'I love you", but 'See there, I knew it all the time.'" For instance, my favorite football team hires a football coach and in my opinion, he isn't the guy I would have selected. I cite all kinds of figures (data) to support my position, but my gridiron comrades ignore me and my words. I sulk away and mumble, "Just you wait. You'll see I'm right." Every year I make the same proclamation and am rebuffed by my ridiculously over- optimistic cohorts. I learn to loathe this coach, because he is making me look foolish in the eyes of my friends. He doesn't make me feel like somebody (and he doesn't even know who I am). I continue to tell others to keep an eye on him, and they'll see what I mean. Every football game I sit anxiously waiting for him to call the wrong play or make a bad decision. Then finally, after fifteen years of winning seasons, he has one losing year. My time to shine as a portender of future events comes and I can proudly announce, "You see there, I told you people he wasn't any good." Selective perception comes in handy when you want to prove a point and make yourself feel like somebody.

We're all familiar with the statement, "Statistics don't lie, statisticians do." In economics, governmental budgeting procedures, criminal and civil court cases, safety regulations, and in a whole host of other very important realms, professionals who no better resort to selective perception to prove their inferences. Limited data are gathered and put forth as the only data that matter in drawing an inference. We're also aquainted with politicians who say they don't pay attention to the polls when the polls aren't in their favor and turn right around to prove their points when the polls are looking good for them. Editorial writers are also notorious for selectively perceiving data to support their positions.

Whether selective perception is right or wrong is for moral thinkers, religious hierarchies, and ethicists. Sometimes the results of selective perception work well because people benefit from this short cut in data gathering. But in our daily lives as we interact with one another in the home and in the workplace, selective perception prevents us from being as effective as we can be. Anytime we limit ourselves to a few pieces of data, we risk neglecting essential facts about people for whom we care. It has seemed to be always true to me that the more data we have, the more effective our inferences will be about ourselves and others.

Next Page: How We Operate-Part 3 Data-Inference-Prophecy Continued