I think we can agree that the summarized barchart is more elegant than the unsummarized one. But

unfortunately, we lost some information when we summarized (averaged) the data. You can no longer

look at the graph and get any sense of how much growth varied.

If you look at the graph below, you can see that one brands produced very **CONSISTENT** growth rates,

and one brand produced very **VARIABLE** growth rates. That is, one brand showed high variability, one

showed low variability, and two showed medium levels. See if you can identify the level of variability for each of these brands —

then put your mouse on the picture to check your predictions. See if you can identify the level of variability for each of these brands — then put your mouse on the picture to check your predictions.

If you turn on javascript, this becomes a rollover |

The standard deviation (SD), which we talked about in module 2, is one way to measure how consistent or

variable a result is. Recall that a small standard deviation would mean the normal distribution is

skinny and tall, and most of the measurements (final fish sizes) are about the same. A large standard

deviation would mean the normal distribution is wide and low, and the measurements (final fish sizes)

are all over the place.

Unfortunately, SD is a bit tricky to calculate, so what we’re

going to do instead is calculate **the AVERAGE deviation, which is an approximation of the standard deviation.**

Say you have 10 fish, with an average length of 100. The first fish actually measures 91 mm, so that one

DEVIATES from the average by 9 mm. The second one measures 105 mm, so that one DEVIATES by 5 mm (notice we are only concerned about the size of the deviation, not whether it is positive or negative.).** If you find all 10 deviations AND
THEN AVERAGE THEM, you will get the average deviation.**

This is very similar to the formula for the standard deviation, except the SD has some squares and square

roots tossed in that make it play nicely with other statistics.

### Fish-O-Matic had the highest variability of any of the four brands. What is the approximate

standard deviation of growth for this brand?

Fish # | 7 | 8 | 9 |
---|---|---|---|

Brand | F-O-M | F-O-M | F-O-M |

Final Length (mm) | 12 | 17 | 22 |

The actual standard deviation is a bit larger than the average deviation — in this case 5 mm rather than

3.33mm. Below is a table of each brand, the average growth, and the standard deviation:

Fish # | 1 to 3 | 4 to 6 | 7 to 9 | 10 to 12 |
---|---|---|---|---|

Brand | BF | BM | FOM | F2W !! |

AVERAGE Growth (mg/day) |
17 | 17 | 17 | 22 |

Average Deviation of Growth (mg/day) |
1 | 3 | 5 | 3 |

**Assuming that **

**growth of fish is normally distributed**(it should be, since many factors contribute), and**that we have accurately measured**the mean and standard deviation of growth for each type of fish food,

**then the distributions of actual growth should look like this: **

But the summarized version of the data that appears in the barchart loses all of this information about the spread of the data.