Statistical manipulation is often necessary to order, define, and/or organize raw
data. A full analysis of statistics is beyond the scope of this work, but there are
some standard analyses that anyone working in a cell biology laboratory should
be aware of and know how to perform. After data are collected, they must be
ordered, or grouped according to the information sought. Data are collected in
these forms:


When collected, the data may appear to be a mere collection of numbers,
with few apparent trends. It is first necessary to order those numbers. One
method is to count the times a number falls within a range increment. For
example, in tossing a coin, one would count the number of heads and tails
(eliminating the possibility of it landing on its edge). Coin flipping is nominal
data, and thus would only have 2 alternatives. If we flip the coin 100 times,
we could count the number of times it lands on heads and the number of tails.
We would thus accumulate data relative to the categories available. A simple
table of the grouping would be known as a frequency distribution, for example:
Similarly, if we examine the following numbers: 3, 5, 4, 2, 5, 6, 2, 4, 4,
several things are apparent. First, the data need to be grouped, and the first task
is to establish an increment for the categories. Let us group the data according
to integers, with no rounding of decimals. We can construct a table that groups
the data.
Mean, Median, and Mode
From the data, we can now define and compute 3 important statistical parameters
The mean is the average of all the values obtained. It is computed by the
sum of all of the values (Σx) divided by the number of values (n). The sum of
all numbers is 31, while there are 9 values; thus, the mean is 3.44.

The median is the midpoint in an arrangement of the categories by magnitude.
Thus, the low for our data is 2, while the high is 6. The middle of this
range is 4. The median is 4. It represents the middle of the possible range of
categories.
The mode is the category that occurs with the highest frequency. For our
data, the mode is 4, since it occurs more often than any other value.
These values can now be used to characterize distribution patterns of data.
For our coin flipping, the likelihood of a head or a tail is equal. Another
way of saying this is that there is equal probability of obtaining a head or not
obtaining a head with each flip of the coin. When the situation exists that there
is equal probability for an event as for the opposite event, the data will be
graphed as a binomial distribution, and a normal curve will result. If the coin
is flipped 10 times, the probability of 1 head and 9 tails equals the probability
of 9 heads and 1 tail. The probability of 2 heads and 8 tails equals the
probability of 8 heads and 2 tails and so on. However, the probability of the
latter (2 heads) is greater than the probability of the former (1 head). The most
likely arrangement is 5 heads and 5 tails. 
When random data are arranged and display a binomial distribution, a
plot of frequency versus occurrence will result in a normal distribution curve.
For an ideal set of data (i.e., no tricks, such as a 2headed coin, or gum on the
edge of the coin), the data will be distributed in a bellshaped curve, where the
median, mode, and mean are equal.
This does not provide an accurate indication of the deviation of the data,
and in particular, does not inform us of the degree of dispersion of the data
about the mean. The measure of the dispersion of data is known as the standard
deviation. It is shown mathematically by the formula:
This value provides a measure of the variability of the data, and in particular,
how it varies from an ideal set of data generated by a random binomial
distribution. In other words, how different it is from an ideal normal distribution.
The more variable the data, the higher the value of the standard deviation.
Other measures of variability are the range, the coefficient of variation
(standard deviation divided by the mean and expressed as a percent) and the
variance.
The variance is the deviation of several or all values from the mean and
must be calculated relative to the total number of values. Variance can be
calculated by the formula:
All of these calculated parameters are for a single set of data that conforms
to a normal distribution. Unfortunately, biological data do not always conform
in this way, and often sets of data must be compared. If the data do not fit a
binomial distribution, often they fit a skewed plot known as a Poisson distribution.
This distribution occurs when the probability of an event is so low that
the probability of its not occurring approaches 1. While this is a significant
statistical event in biology, details of the Poisson distribution are left to texts on
biological statistics.
Likewise, one must properly handle comparisons of multiple sets of data.
All statistics comparing multiple sets begin with the calculation of the parameters
detailed here, and for each set of data. For example, the standard error of
the mean (also known simply as the standard error) is often used to measure
distinctions among populations. It is defined as the standard deviation of a
distribution of means. Thus, the mean for each population is computed and the
collection of means are then used to calculate the standard deviation of those
means.
Once all of these parameters are calculated, the general aim of statistical
analysis is to estimate the significance of the data, and in particular, the probability
that the data represent effects of experimental treatment, or conversely,
pure random distribution. Tests of significance will be more extensively discussed
in other volumes.
