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Week 5 Lecture

Il ne faut pas se fier aux apparences.

Assessing Validity of Key Assumptions

An assumption is a belief that something is true without proof supporting the opinion. Facts do not necessarily support assumptions. Assuming a topic is much easier than conducting a critical thinking process to interpret information.

Assuming is not a bad thing in and of itself. It can be a good starting point in the intelligence process. Assumptions made by analysts can be helpful to begin to build the intelligence product. The professional training, education, and experience of the analyst can serve to produce reasonable assumptions. But the analyst must conduct a critical examination of assumptions since many assumptions are unfounded or wrong. According to your text, about one in four assumptions is not supported when critically examined.  

Critical examination of key assumptions and distinguishing the differences between assumptions, assessments, and information

A systematic approach should be employed by the analyst to critically examine assumptions. Analysts can use a process known as the Key Assumptions Check to discover gaps in the assumptions and what may be factual. The Key Assumptions Check is a straightforward way to begin an intelligence analysis. This process can be used anytime during the project. However, using it in the early stages of the process can save wasted time by identifying incorrect assumptions before the later stages of the project.

An eight-step process is used to conduct the Key Assumptions Check:

1. Involve a small group of analysts. Add “outsiders” that can challenge common thinking.
2. List assumptions.
3. Post assumptions so they are visible to all in the group.
4. Use the procedure of answering the Five W’s and an H to analyze the strength of the assumptions.
5. Critically examine each assumption by posing additional questions.
6. Categorize each assumption as either solid and well-supported, correct with caveats, or unsupported/questionable. These are known as key uncertainties.
7. Delete questionable assumptions and add any assumptions that were developed in the process.
8. Consider further research into unsupported assumptions and key uncertainties.

There are differences between what is information, assumptions, judgments, and assessments (see Figure 11.3). The analyst must recognize the differences. Information includes factual data about a situation. Assumptions are the analyst’s opinion about the information. Judgments are derived from facts and analysis. Assessments are judgments regarding unknown occurrences and what the implications are regarding an event or issue. 

There are differences between what is information, assumptions, judgments, and assessments (see Figure 11.3). The analyst must recognize the differences. Information includes factual data about a situation. Assumptions are the analyst’s opinion about the information. Judgments are derived from facts and analysis. Assessments are judgments regarding unknown occurrences and what the implications are regarding an event or issue. 

Preparing an Analytic Argument

The job of the analyst is to provide useful information about subjects of interest to their clients that cannot be obtained from other sources. The information provided should be valuable for the client to make decisions and take action. Analysts inform their clients of their conclusions through arguments or sets of statements that contain a conclusion supported logically by the other arguments or statements. Arguments are built by analysts through a series of statements. The argument contains a conclusion statement that is supported by other statements.

Creating effective arguments is not an easy task for an analyst. The text identifies the following methods for developing expertise to produce effective analytic arguments.

Study cases.

Identify patterns within cases.

Generalize patterns across cases.

Formulate hypotheses.

Create models.

Test Models. 

Analysts should distinguish differences in claims, reasons, and evidence when preparing an argument as these terms can be used in different ways.

Recognizing Logical Fallacies and Avoiding Weakly Constructed Arguments

Analysts need to provide logical arguments. The two types of formal logic are induction and deduction. Deductive reasoning is a process of examining information that is known to reach a logical conclusion. Deductive reasoning begins with a general premise or observation and gets the analyst to a specific conclusion. Conclusions can be considered valid if the premises that an argument is based on are valid. Inductive reasoning involves examining specific cases or issues to construct arguments. This form of reasoning begins by examining specific instances and then going to a general conclusion. However, with inductive reasoning, the conclusions of the analyst may be incorrect even if the argument is valid.

Fallacies of logic are flaws in reasoning that lead to improper conclusions. Fallacies prey on emotions, not logic. Some of the most dangerous logical fallacies to avoid are false analogies, ad hominem arguments, hasty generalizations, false dichotomy, and post hoc, ergo propter hoc arguments.

NOTE: put the following information into a table.

False analogies are comparisons of two cases that are not similar in nature. The cases may seem similar, but they may not be.

Example: “Cars cause more deaths than firearms do, so if we are going to ban firearms, we should also ban cars.”

Ad hominem arguments attack the person making the argument or statement. The credibility, personality, or integrity of the person making the claim is the subject of action taken to discredit the argument or statement, rather than the validity of the argument or statement.

Hasty generalizations are statements or arguments made on insufficient evidence. They are oversimplifications and often use stereotypes to support the argument.

Example: “Of course that doctor advocates vaccination—he probably owns stock in a pharmaceutical company.”

False dichotomies reduce overly complicated issues to only two options for consideration.

Example: “Cars cause more deaths than firearms do, so if we are going to ban firearms, we should also ban cars.” 

Post hoc, ergo propter hoc arguments argue that one incident is the cause of another incident, but the cause is false.

Example: “My child was diagnosed with autism after receiving vaccinations. That is proof that vaccines are to blame.”

 You can find more examples of logical fallacies here: https://englishcomposition.org/advanced-writing/logical-fallacies-examples/.