Transcript Rule 1 - Blended Biology

Introduction to lab data
Stickrath
What does “mean” mean?
• Click on the link below…be sure you read
about the following terms: mean, median,
mode, range
• http://math.about.com/od/statistics/a/Mean
Median.htm
Why are “trend lines” trendy?
• Click on the link below
• http://www.visionlearning.com/library/modu
le_viewer.php?mid=109
How can “error bars” be
erroneous?
• Error bars may show standard errors (SE), or standard
deviations (SD).
• Different types of error bars give quite different
information.
• Descriptive error bars. Range and standard deviation
(SD) are used for descriptive error bars because they
show how the data are spread.
– Range error bars encompass the lowest and highest values.
– SD error bars are calculated using the SD equation
• SD = √[(sum of each value – mean)2/n]
• Inferential error bars. In experimental biology it is more
common to be interested in comparing samples from two
groups, to see if they are different.
– Standard Error (SE) is defined as SE = SD/√n.
Rules for Error Bars
• Rule 1: when showing error bars, always
describe in the figure legends what they are.
• Rule 2: the value of n (i.e. the sample size) must
be stated in the figure legend.
• Rule 3: because experimental biologists are
usually trying to compare experimental results
with controls, it is usually appropriate to show
inferential error bars, such as SE, rather than
SD. However, if n is very small (for example n =
3), rather than showing error bars and statistics,
it is better to simply plot the individual data
points.
• What can you conclude when standard error bars do not overlap?
– When standard error (SE) bars do not overlap, you cannot be sure that
the difference between two means is statistically significant. Even
though the error bars do not overlap in experiment 1, the difference is
not statistically significant.
– You must use a t-test or chi-square test to compare data
• What can you conclude when standard error bars do overlap?
– When SE bars overlap, (as in experiment 2) you can be sure the
difference between the two means is not statistically significant