Psychology 101: Intro to Psychology

What are the two main types of statistics used by psychologists? In this lesson, you'll start to see what psychologists need to do to analyze their data and test the significance of their results.

Once psychologists have carefully chosen a study design appropriate for their subjects, thought carefully about their variables and measurements, selected a sample group and run their tests, they're typically faced with a mountain of data. It could be anything from survey results to maps of brain activity. In order to make the experimental process worthwhile, psychologists must now find ways to interpret and draw conclusions from their data. They ultimately want to test whether the data supports or rejects their hypothesis.

In order to do this, psychologists use statistical analysis. They make use of two main types of statistics: descriptive and inferential. Descriptive statistics help psychologists get a better understanding of the general trends in their data, while inferential statistics help them draw conclusions about how their variables relate to one another.

Descriptive statistics are basically what they sound like: they describe and summarize a set of data. Descriptive statistics could be things like the average age of participants or how many were men and women. Your GPA is a descriptive statistic; it summarizes how you've done in school. These kinds of statistics generally make use of averages, also known as the central tendency of the data, to summarize the data set. There are three kinds of averages that you may have learned about in math class: mean, median, and mode. The mean is what's most commonly associated with average; it's when you add up a set of numbers and then divide by how many are in the set. Let's say you did a survey of how many donuts per week your neighbors eat. Only five of your neighbors respond, giving you a data set that looks like this: {1, 2, 2, 2, 13}. The mean number of donuts your neighbors eat is (1+2+2+2+13)/5, or four. But since one of your neighbors is an outlier and eats way more donuts per week than the others combined, the median or mode might be a better measure of central tendency for this data set. The median of a set is just the number that divides the set in half if you've ordered it from least to greatest - so in this case, two, or the number in the middle. The mode is the most frequently repeated number in the set - in this case, also two. You can remember mode by just replacing the last two letters--mode is 'most.' Though the mean is often a great tool for measuring central tendency, in this case two donuts per week is much more realistic than four.

Descriptive statistics also address the dispersion of a set, or how widely its elements vary. The standard deviation and variance are related measures that can give psychologists a sense of this for their data. Both tell psychologists how far from the mean an individual data point is likely to be. So while the data set of your donut survey has an outlier (13), the equations to calculate standard deviation and variance take the probability of each result into account. Since 13 doesn't have as high of a probability as two, it doesn't weigh as heavily into calculating how widely responses are likely to vary.

Inferential statistics can be used to draw conclusions from the data that descriptive statistics describe. Researchers can look at their data and determine how likely it is that changes in one variable caused changes in another or that two variables seem to be related to one another. These conclusions can help them determine whether the data supports or rejects their hypothesis. Let's say you conducted a few other surveys of your neighbors, attempting to relate donut consumption to weight. You get results back that seem to confirm your hypothesis that higher donut consumption is associated with higher weight; the 13 donut per week neighbor is the heaviest of the bunch. But before you condemn donuts, you need to show that your results have statistical significance. When psychologists look at data, they perform a variety of statistical tests to confirm that their correlations aren't just a result of chance. Psychologists have agreed that if a result has a less than five percent chance of occurring due to chance, it can be called statistically significant. If results are significant, they can be used to support or reject hypotheses.

Statistical analysis is a complicated and important part of psychological research. We've introduced some of the major concepts and terms: descriptive statistics that summarize a data set and inferential statistics that help researchers draw conclusions from it. Descriptive statistics make use of central tendency, or averages, and measures of dispersion like the standard deviation. Inferential statistics help to determine the statistical significance of a data set. Only if results are significant can they be used in support of a hypothesis.