Making Sense of Research

by Jamie Hale

In the context of everyday language, statistics (numbers, quantitative representations) are used to represent basketball players’ free throw average, death rates, life spans and so on. In science, statistics are tools used in describing, organizing, summarizing and analyzing data. Learning about stats will help you think in terms of probabilities, and allow you to gain a better understanding of research data.

Descriptive statistics are numerical measures that describe a population by providing information on the central tendency of the distribution, the width of distribution (dispersion, or variability), the shape of distribution (Jackson, 2009). Inferential statistics are procedures that allow us to make an inference from a sample to the population. That is, we are able to make generalizations about a population based on the information derived from the sample.

Why we need statistics

A key reason we need statistics is to be able to effectively interpret research. Without statistics it would be very difficult to analyze the collected data and make decisions based on the data. Statistics give us an overview of the data and allow us to make sense of what is going on. Without statistics, in many cases, it would be extremely difficult to find meaning in the data. Statistics provides us with a tool to make an educated inference.

Most scientific and technical journals contain some form of statistics. Without an understanding of statistics, the statistical information contained in the journal will be meaningless. An understanding of basic statistics will provide you with the fundamental skills necessary to read and evaluate most results sections. The ability to extract meaning from journal articles, and the ability to evaluate research from a statistical perspective are basic skills that will increase your knowledge and understanding of the article of interest. Quantitative research uses stats, and to assess statistical validity, at least a basic understanding of stats is indispensable.

Gaining knowledge in the area of statistics will help you become a better-informed consumer. To reiterate, if you understand basic statistical concepts, you will be in a better position to evaluate the information you have been given.

Beware of Person-Who Statistics

Results of scientific studies are stated in probabilistic terms. Science is not in the business of making claims of absolute certainty (refer to bead model of truth). When science describes, predicts or explains something, it is understood that the conclusion is tentative. This willingness to admit fallibility is one of science’s biggest strengths. In virtually every other area of knowledge acquisition, admitting fallibility is not a virtue, but a weakness.

Person-who statistics: situations in which well-established statistical trends are questioned because someone knows a “person who” went against the trend (Stanovich, 2007). For example, “Look at my grandpa, he is ninety years old, has been smoking since he was in thirteen, and is still healthy”, this statement is implying smoking is not bad for health. Learning to think probabilistically is an important trait, and can lead to more accurate thinking. Person-who statistics is a ubiquitous phenomenon.

People like assertions that reflect certainty. Statistical, scientific thinking is not about absolute certainty. The conclusions drawn from scientific research are probabilistic- generalizations that are correct most of the time, but not every time. People often weight anecdotal evidence more heavily than probabilistic information. This is an error in thinking, leads to bad decisions, and often, irrational thinking.

Confused about Correlation?

Correlation does not necessarily imply causation, but that doesn’t mean correlation is not important. Two variables may be associated without having a causal relationship. However, just because a correlation has limited value as a causative inference, does not mean that correlation studies are not important to science.

Why are correlation studies important? Stanovich (2007) points out the following:

• First, many scientific hypotheses are stated in terms of correlation or lack of correlation, so that such studies are directly relevant to these hypotheses.
• Second, although correlation does not imply causation, causation does imply correlation. That is, although a correlational study cannot definitely prove a causal hypothesis, it may rule one out.
• Third, correlational studies are more useful than they may seem, because some of the recently developed complex correlational designs allow for some very limited causal inferences.
• …some variables simply cannot be manipulated for ethical reasons (for instance, human malnutrition or physical disabilities). Other variables, such as birth order, sex, and age are inherently correlational because they cannot be manipulated, and, therefore, the scientific knowledge concerning them must be based on correlation evidence.

When researchers say that variables are correlated what do they mean? Correlation between variables implies as one variable changes, the other variable has a tendency to change. That doesn’t mean the one causes the other. A correlation claim involves at least two variables, and the variables are measured, but not manipulated. The correlation coefficient r is the measure of the degree of relationship between scores. It can vary between –1.00 and +1.00.

A positive correlation occurs when variables change together in the same direction. As an example, if one goes up the other also goes up, and if one goes down the other also goes down. A negative correlation (inverse association) occurs when variables move in opposite directions. As an example, when one goes up the other goes down, or when one goes down the other goes up.

Stats made EZ

Statistics are difficult for many people. Students often cringe when they hear the word - statistics. Learning about statistics requires the same strategies as learning about other topics (strategies to improve learning and memory). Once an individual learns theoretical aspects and calculations used for basic statistical procedures the learning of more complex statistics become much easier.

Everyone benefits from learning the basics of statistics. Statistics is not an easy subject compared to many other subjects, but the subject is much easier when one doesn't have negative expectations and realizes that with the appropriate cognitive effort and understanding of some rather basic mathematical principles the subject is learnable. Being knowledgeable in the area of statistics will be beneficial across domains of scholarly and everyday life.

Recently I asked Dr. Jonathan Gore the following question- Why is a basic understanding of stats important for the public?

"My answer to why stats is important is that pretty much everything operates based on probability. Even some of the "hard" sciences are starting to realize that phenomena that used to only require a basic equation are now having to factor in probability to account for all that they observe. To understand events that occur in our daily lives, including understanding other people’s behaviors, the economy, and health, we have to address probabilities rather than basic equations."

To learn more about stats and research methods read- In Evidence We Trust: The need for science, rationality and statistics (Hale, 2013). The book contains 76 questions and answers regarding scientific research methods and stats. It also contains practice problems involving statistical procedures. References are available upon request (jamie.hale1@gmail.com).