PSYC 2030 Lecture Notes - Lecture 2: Central Tendency
29 May 2018
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PSYC 2030 Lecture 2 Notes
Introduction
Measures of Central Tendency
• One way to do this is to convert the data into a simple bar graph, on the next page,
which displays a distribution of different brands of trucks still on the road after a
decade.
• When reading statistical graphs such as this, take care
• It’s easy to desig a graph to ake a differee look ig or sall
• The secret lies in how you label the vertical scale (the y-axis).
• The point to remember: Think smart.
• When viewing graphs, read the scale labels and note their range.
• The next step is to summarize the data using some measure of central tendency, a single
score that represents a whole set of scores.
• The simplest measure is the mode, the most frequently occurring score or scores.
• The most familiar is the mean or arithmetic average— the total sum of all the scores
divided by the number of scores. The midpoint— the 50th percentile—is the median.
• On a divided highway, the median is the middle.
• So, too, with data: If you arrange all the scores in order from the highest to the lowest,
half will be above the median and half will be below it.
• Measures of central tendency neatly summarize data.
• But osider what happes to the ea whe a distriutio is lopsided, whe it’s
skewed by a few way-out scores.
• With income data, for example, the mode, median, and mean often tell very different
stories
• This happens because the mean is biased by a few extreme scores.
• When Microsoft co-founder Bill Gates sits down in an intimate café, its average (mean)
customer instantly becomes a billionaire.
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