Ch 1.5 and Appendix B Flashcards
5 steps of Biological Studies
- Experimental Design
- Data Collection
- Organize and Visualize Data
- Summarize the Data
- Inferential Statistics
Features of a Sample
Should be representative of a larger population
Quantitative Variables
Variables on a numerical scale (ex: temperature, size, etc…)
Discrete Variables
Quantitative variables that only take on whole number values
Continuous Variables
Quantitative variables that take on only fractional values
Categorical Variables
Variables that take categories as values (Blood Types)
Ordinal Variables
Categorical variables with natural ordering (ex: final grades in Bio - A,B,C,D,F)
Frequency Distribution
Lists all possible values and the number of occurrences of each value in the sample
Histograms
Depict frequency distributions for quantitative data.
Scatter Plot
Used most often to compare two quantitative variables
Linear Relationship
When the points of two variables fall on a straight line in a scatter plot graph
Statistic
Numerical quantity calculated from data
Descriptive statistics
Quantities that describe general patterns in data
Describing categorical data
Use proportions
Mean
Aveage
Median
The number literally in the middle. If there are an even amount of numbers (2 middle numbers) you take the average of those two numbers
Mode
The most commonly occur number
Measures of Dispersion
Tell us how much values differ from eachother
Standard Deviation
Calculates the extent to which the data are spread out form the mean
Correlation Coeffecient
Quantifies the strength of the relationship between two quantitative variables using a single value
Linear Regression
Fits a straight line to data, minimizes residuals
Residuals
The vertical distances between the points in the scatter plot and the linear regression line itself
Alternative Hypothesis
The opposite of the Null Hypothesis
Bayesian inference
A statistical approach that makes it easier to favor a new hypothesis
Frequentist Statistics
Traditional statistical methods
Type I Error
Rejecting the null hypothesis when it is actually true
Type II Error
Accept the null hypothesis when it is actually false
P-value
The likelihood that chance alone would produce data that differ from the null hypotheses as much as our data differ from the null hypothesis
Significance level
A p-value threshold.
The null hypothesis can only be rejected when the P-value is less than or equal to the significance level (alpha = 0.05)
This limits Type I error to 5%
Power
The probability that we will correctly reject the null hypothesis when it is false.
The higher the power, the less likely we are to make a Type II error
3 Ways to Increase Power
- Decrease the significance level (alpha) - the higher the value of alpha, the harder it is to reject the null, even if it is actually false
- Increase the sample size
- Decrease variability in the sample - the more variation there is in the sample, the harder it is to discern a clear effect when it actually exists
Natural History
The characteristics of a group of organisms (How they get their food, reproduce, behave, regulate their bodies) and how they interact with other organisms
Quantify
Assigning numerical values to observations through measurement
Hypothesis
A tentative answer to a question, from which TESTABLE predictions can be generated
Deductive logic
Used to make predictions based on the hypothesis. Starts with a believed to be true statement and goes on to predict what facts would have to be true to be compatible with the statement
Controlled Experiment
Sample is divided into groups and these groups are exposed to manipulations while one group serves as an untreated control. Data then tells if there are changes in a dependent variable as a result of the manipulations