Stats Test 1- Central Tendency

Front 1

Central Tendency

Back 1

scores seem to pile up around some central clustering point

- At the extreme low and extreme high not many people

- Average will have the highest

Front 2

Variability

Back 2

How much dispersion or spread the distribution has.

There is a certain amount of scatter around this central clustering point.

-greater the variability w/ a wider range

-smaller the variability w/ a narrow range

Front 3

Skewness

Back 3

Distribution departure from normality (symmetry) mirror

The spread of the scores around the central clustering point may not be symmetrical. Instead , there may be more spread in the scores on one side than the other side.

- Zero skew: Mirror image

- Negatively skewed: Spread is towards the left side (higher end) of the X-axis

-Positively skewed: Spread is towards the right side (lower end) of the X-axis; towards the Y-axis

Front 4

Kurtosis

Back 4

How tall and peaked vs. how flat the distribution

The peak of the central clustering point may be steeped or gently rounded.

Leptokurtic: tall and narrow peak

Platykurtic: rounded peak

Misokurtic: normal; symmetry- mirror image

Front 5

Realationship btw number of peaks and variability

Back 5

If a graph has two peaks it will be considered having a central tendency if the distribution still follows the distribution rule b/c towards the middle still have most variable

Front 6

Mode

Back 6

The simplest measure of central tendency

Is the most frequently occurring score in a distribution of scores.

Not useful measure for central tendency- finding the average is better.

- Arrange scores from lowest to highest

- Can have more than ___ (two peaks)

Front 7

Average/Mean

Back 7

By far the most common used measure of central tendency. Represents the centroid; it is the balancing point of any distribution of scores

- Add up all scores divide by total number of scores

Front 8

Advantages of the mode

Back 8

1) Very simple to compute

2) ___ can be used regardless of the level of measurement of the scores

Front 9

Disadvantages of the mode

Back 9

1) It is completely determined by only a few of the scores; no matter how many scores there are, make no contribution to the ___ at all.

2) ___ determine only by a few scores and is unaffected by the rest of the scores, it is unstable

3) Higher level analysis cannot be done using the ____; it is often a dead end

Front 10

Median

Back 10

The middle score in a distribution. Half the scores in the distribution will fall above the ___, and other half will fall below it.

- If distribution contains an odd number of scores, the ___ will be the score w/ equal numbers above and below it.

If you have an even number of scores you don't have a true middle point when. Find the ___ by adding the two middle scores dividing by two.

Front 11

When do you use the median?

Back 11

Use when you have extreme scores

Front 12

Advantage of the median

Back 12

It is not excessively influenced by extreme scores

Front 13

Disadvantages of the median

Back 13

1) Tends to be unstable in sampling

2) Can't use in many higher level analysis

3) Gives no extra weight to extreme scores. Flip side to its advantage

Front 14

Disadvantages of the Mean

Back 14

1) it requires scores that are either interval or ratio level of measurement. When this requirement is not met must use another measure of central tendency.

2) Extremely large or extremely small scores can gave too much influence on the value of the _____. Since every score is added when computing for the ___. Even one extreme high or low score can cause the mean to be an inaccurate description of the value of a typical score.

Front 15

Advantages of the Mean

median and mode

Back 15

1) It is the measure of central tendency that is the most stable in sampling. It would show less chance variation than either ___ and _____

2) Only measure of central tendency that is influenced by the exact value of every score in the distribution; unlike ____ and ____.

3) Basis of many of the higher level analysis and unlike ___ and ___ is not a dead end.