Chapter 12: Quantitative Skills and Biostatistics

There are six types of graphs you should be familiar with:

A graph must include the following things:

A title, measured axes labeled with numbers, labels and units, and index marks a frame or perimeter data points that are clearly marked.

Count data are generated by counting the number of items that fit into a category.
Normal, or parametric, data is measurement data that fits a normal curve, or normal distribution, usually for a large sample
The sample size (n) refers to the number of members of the population that are included in the study.
The mean (x) is the average of the sample
One limitation of mean is, it is influenced by outliers
Nonparametric data often includes large outliers and do not fit a normal distribution.

Hypothesis: A prediction of what the outcome of the experiment will be.
Independent Variable: The factor that you, as the experimenter, will change between the different groups in the experiment
Dependent Variable: The data that you measure during the experiment.
Constants (Controlled Variables): The things that are the same
Control Groups: Any group that is needed so you can.
Statistical Significance: The trustworthiness of the results and the certainty you have in your conclusions.
Time-course experiments look at how something changes over time. A line graph is usually used to present this type of data.
Bar graphs are helpful to compare categories of data
Box-and-whisker plots should be used for nonparametric data
Association experiments look for associations between variables. They attempt to determine if two variables are correlated, and additional tests can demonstrate causation.
Scatterplots are used to present data from association experiments.

P = a/n

The probability (P) that an event will occur is the number of favorable cases (a) divided by the total number of possible cases (n).

The product rule is used for independent events and is also called the “AND rule.”
The sum rule is used for studying two mutually exclusive events, and can be thought of as the “EITHER” rule.

Hypothesis testing is used to determine if two groups are significantly different from each other.
It starts with a null hypothesis, which is rejected or accepted, depending on how a calculated p-value or chi- square value compares to a standard value.
Many experiments involve comparing two datasets or two groups, and a t- test can be used to calculate whether the means of two groups are different from each other.
This test is most often applied to datasets that are normally distributed. A p-value equal to or below 0.05 is considered significant in most biology-related fields.
A chi-square test is a statistical tool used to measure the difference between observed and expected data.