Developing & Evaluating Inferences From Data

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  • 0:04 The Great Detective
  • 0:41 Inferences
  • 1:05 Types of Data
  • 1:59 Evaluating Validity
  • 3:43 Example
  • 4:24 Lesson Summary
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Lesson Transcript
Instructor: Della McGuire

Della has been teaching secondary and adult education for over 20 years. She holds a BS in Sociology, MEd in Reading, and is ABD on the MComm in Storytelling.

In this lesson, we will look at what it means to make inferences and how to use data to inform those inferences. We will also look at how to determine if your data is a valid and reliable sources of information from which to develop accurate inferences.

The Great Detective

In The Sign of the Four by Sir Arthur Conan Doyle, the great detective and father of modern investigations, Sherlock Holmes, refers to the importance of making inferences from data:

'While the individual man is an insoluble puzzle, in the aggregate he becomes a mathematical certainty. You can, for example, never foretell what any one man will do, but you can say with precision what an average number will be up to. Individuals vary, but percentages remain constant. So says the statistician.'

In this quote, Holmes is paraphrasing Scottish philosopher and anthropologist William Winwood Reade.

Inferences

Making an inference refers to the process of taking information you already know, adding it to new knowledge from reliable data, and developing a conclusion by integrating them. Readers must make inferences by 'reading between the lines' to have greater comprehension of the text. Similarly, inferences can be made about human behavior based on collections of information about many people.

Types of Data

The information used to increase knowledge, say, about human behavior, is called data. It may come from a scientific study on behavior and psychology, it may be in the form of compiled surveys and statistics in a quantitative method, or may come from qualitative sources that tell a story, like case studies or anecdotes.

Quantitative data collection methods measure the quantity, prevalence, or numbers of incidents about a specific situation. This emphasizes a 'macrosocial' view of looking at human behavior in a large scale, big picture view from a societal or global perspective.

Qualitative data collection methods tell about the individual experiences of those incidents about a specific situation. This emphasizes the 'microsocial' view of looking at human behavior in a small scale, individual, interpersonal, familial and small group perspective.

Evaluating Validity

Experimental Design

Qualitative or quantitative data must be valid in order to develop an accurate inference. First, research must rely on sound principles of scientific inquiry, using the scientific method and ethical guidelines on research with human subjects.

In cases of quantitative data, you must ensure that the number of study participants is a generalizable sample size. This means that if a survey includes too few participants, it cannot be extrapolated to a wider population. If a survey is done on a large scale and includes a valid random sample of the relevant population, then that survey is more valid and reliable for drawing an inference.

Bias

Another issue that can influence the reliability of data is the source of the research. People are often paid to generate favorable results for the entity seeking the research. One infamous example is milk. Remember the slogan, 'Milk. It Does a Body Good'? That campaign was paid for by the California Milk Advisory Board who had a vested interest in influencing the research. If someone stands to make money from certain results, they may be intentionally or unintentionally bias in their inferences.

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