Chapter 2 - Key Words Flashcards
Raw scores
For example if a student gets 17 questions right on a 20-question test then his raw score will be 17 (his percent score would be 85, but his raw score would be 17). Raw scores are usually numbers, but they can technically be other things like names.
Frequency
Let’s keep the example of that guy who got a 17 on the test. Now let’s say that he’s in a class of 40 students and 5 other students also got a raw score of 17. The frequency of that raw score would be 6.
Frequency distribution
A chart/graph that shows how many times each raw score occurred.
The raw scores are placed across the bottom of the chart, and the frequencies on the vertical part of the chart.
Bar graph
A frequency distribution graph for nominal and ordinal data where the vertical bars don’t touch each other. Each vertical bar shows the frequency of its corresponding raw score (which is given below the graph’s baseline)
Histogram
A special type of distribution where all the vertical bars that measure frequency are placed side-by-side against each other. It is used for interval/ratio data.
Frequency polygon
It’s the same thing as a histogram, but it uses a connect-the-dots pattern like a line graph, instead of the vertical bars pattern that a histogram uses.
It’s used when you have lots of different raw scores (more than 7) and you don’t want to waste the time or the ink it would take to make a histogram. A frequency polygon is a lot simpler to read as well, it is less “busy.”
“Busy” in this case means that a graph is cluttered and difficult to interpret quickly.
Data point
A dot (or bar) on a graph
Grouped distribution
A bar graph, histogram, or frequency polygon where the raw scores are grouped into ranges rather than kept separated. In this case, each vertical bar (or data point) represents a RANGE of scores rather than a specific score.
They use these when there’s tons of different raw scores in the data.
Normal curve
The graph of a normal distribution, frequently called the “bell curve.”
Normal distribution (of scores)
A distribution where most scores are in the middle, and scores get progressively less frequent the further you go out from the middle. It also has symmetry.
Tail of the distribution
The section of the distribution furthest from the mean, where very few scores are found. By the way, this only applies to distributions of interval/ratio data.
Often times, your distribution has TWO tails, one to the left (negative) and one to the right (positive)
Negatively skewed distribution
A distribution with some scores far below the mean that pull the mean lower.
Positively skewed distribution
A distribution with some scores far above the mean that pull the mean higher.
When there are FEW, you found the SKEW. So if a distribution has very few high scores then it is positively skewed.
Bimodal distribution
A distribution with two “humps” which show high frequencies.
This often happens when there are two distinct groups represented in your data, like men and women.
Bimodal distribution
A distribution with two “humps” which show high frequencies.
This often happens when there are two distinct groups represented in your data, like men and women.
Proportion of the area under the curve
I’m not going to cover this, either. It’s a lot easier to understand this concept once you start working with some of the formulas anyway, and we haven’t gotten to that part yet.
Relative frequency
Don’t worry about it. This will be easier to understand after chapter 4
Percentile
The PERCENT of scores that are AT or BELOW a selected score.
So, a score in the 55th percentile is higher than or equal to 55 percent of the sample’s scores. The highest percentile score you can have is a 99, because a 100th percentile score would imply that you scored higher than all the data, but since your score is part of the data that can’t happen. You can score higher than everyone else (99th percentile) but you can’t score higher than everyone including yourself, so 100th percentile scores don’t exist.
Cumulative frequency
The NUMBER of scores at or below a particular score.
Let’s say we have a class of 50 students, and one student gets a 100 percent on a test. That students PERCENTILE would be 99th but their CUMULATIVE FREQUENCY would be 50 since there are only 50 students in the class.
