Biology Reference
In-Depth Information
is visible on the storage box before you open it, you will be affected by that whether or not you
intend to. Ideally, you should cover up the age/sex/ancestry information on the boxes of
those skeletons you will use for your study ahead of time (a day or so in advance so you
will not remember information about specific cases). If time is a factor, then have a helper
do this for you. Additionally, other bones in the box that you are not analyzing will also cause
bias as you see them while unpacking the box to pull out the bone(s) you need. If you can,
have a helper pull out the bones you will be analyzing. This will ensure that each observer
is “blind” to other information that may lead to bias.
Each study will be different with regard to sample size. For some, such as certain paleo-
pathological case studies, the sample size is obviously one. For other studies, a sample
size of one is unacceptable. In order to have statistical significance, you must choose a sample
size that will be appropriate based on the population variance of the measurements or traits
being studied (
Buikstra and Ubelaker, 1994
). Choosing an appropriate sample size can be
tricky and confusing, especially since many introductory statistics courses teach that a sample
of 30 is large enough for statistical significance, which is unwise (L. Konigsberg, personal
communication 2012). Referring to statistical texts and previously published articles in
your topic will help you choose the most appropriate sample size for your study. For infor-
mation on important factors to consider when choosing a collection and sample, especially
with regard to inherent bias, see
Box 2.1
.
The data collection method will rarely be something novel. If you are validating
a stature equation for example, then you will follow the same method for measuring
BOX 2.1
BIAS
Types of Bias
In addition to the
osteological paradox
(see DiGangi and Moore [Chapter 1] and
Smith [Chapter 7], this volume for detailed
discussion), there are several types of bias
with skeletal collections: (1) interment, (2)
archaeological,
determine who is buried where. See Smith
(Chapter 7), this volume for details.
For archaeological bias, only those sites
discovered and excavated will contribute to
the sample. If the archaeological recovery
was focused on houses for example, burials
located elsewhere may have been over-
looked. See Smith (Chapter 7), this volume
for more information.
In terms of recovery bias, only so many
sites are discovered and excavated for
a variety of reasons, resulting in collections
that are mere fractions of once living pop-
ulations (
White et al., 2012
). We then use
the
(3)
recovery,
and
(4)
taphonomic.
Interment bias refers to the fact that not all
individuals have an equal chance of being
buried at a certain location due to the
particular mortuary program in place. For
example, infants may not be buried at all and
social status or other cultural reasons may
individuals
in these
collections as
(Continued)
