Textbook Notes (368,986)
Canada (162,320)
Biology (363)
BIOL 300 (1)
Chapter 1

Bio 300 Ch. 1.docx

6 Pages
163 Views
Unlock Document

Department
Biology
Course
BIOL 300
Professor
Michael Whitlock
Semester
Fall

Description
Chapter 1: Statistics and Samples 1.1- What is Statistics? - Biologists study properties of living things - Measuring properties of living things is a challenge because: o No two individuals of a population are exactly alike o Can’t measure everyone in a population due to time constraint and limited funding - Measuring properties of living things are done through a sample of individuals drawn from the population - Sampling brings uncertainty to the measurements because properties of the sample are not the same as the true values in the population - Statistics- technology that describes and measures aspects of nature from samples; makes it possible to quantify the uncertainty in measures - Estimation- process of inferring an unknown quantity of a target population using only sample data o Estimation allows us to assess differences between groups and relationship between variables - Parameter- quantity describing a population; “the truth”; ex. averages, proportions, measures of variation, measures of relationship…etc - Estimate- related quantity calculated from a sample; “approximation of the truth, subject to error” - Statistics tells us how best to estimate these parameters using our measurements of a sample - By measuring every possible member of the population, we could know the parameter without error, but that is rarely possible - Instead, we use estimates based on incomplete data to approximate the true value - Hypothesis testing- process of determining how well a “null” hypothesis about a population quantity fits a sample of data o Null hypothesis- specific claim regarding the population quantity; made for the purposes of argument and often embodies the skeptical point of view 1.2- Sampling Populations - Our ability to obtain reliable measures of population characteristics and to assess the uncertainty of these measures depends on how we sample populations  Populations and Samples - Population- entire collection of individuals or units of interest; composed of large number of individuals; ex. all cats that have fallen from buildings in NYC - Sample- subset of individuals or units taken from the population; smaller set of individuals; used to draw conclusions that apply to whole population; ex. fall cats brought to one veterinary in NYC o Sometimes a basic unit of sampling is literally a single individual or a group of individuals o Scientists use several terms to indicate the sampling unit, such as “unit”, “individual”, “subject”, or “replicate”  Properties of Good Samples - Estimates based on samples typically depart somewhat from the true population characteristic - Sampling error- chance different between an estimate and the population parameter being estimated o Larger samples are less affected by chance than smaller samples therefore will have lower sampling error and higher precision - Precision- indicated by spread of estimates resulting from sampling error - Accuracy (Unbiased)- indicated by distance from the true mean value - Bias- systematic discrepancy between estimates and the true population characteristics - Major goal of sampling is to minimize sampling error and bias in estimates - Ideally, all the estimates are tightly grouped, indicating low sampling error, and they are centered on the bull’s-eye, indicating low bias o Estimates are precise if they are tightly grouped and highly repeatable o Estimates are imprecise if they are spread out and not repeatable o Estimates are accurate (unbiased) if they are centered on the bull’s- eye o Estimates are inaccurate (biased) if they are displaced systematically to one side of the bull’s-eye - Both precision and accuracy are important because lack of either means that an estimate is likely to differ greatly from the truth - Final goal of sampling is to allow the precision of an estimate to be quantified  Random Sampling - Random sample- sample in which each member of the population has an equal and independent chance of being selected - For a random sample, every unit in the population must have an equal chance of being included in the sample o Hard-to-sample individuals must be included in the sample, otherwise it would lead to bias due to their differences in characteristics from the rest of the population - For a random sample, the selection of units must be independent – the selection of any one member of the population must in no way influence the selection of any other member o Individuals from the same household cannot be included in the sample because it makes the sample effectively smaller and causes the data to be skewed - Random sampling minimizes bias and makes it possible to measure the amount of sampling error - How to take a random sample (Method 1): o Create a list of every unit in the population of interest and give each unit a number between 1 and the total population size o Decide on the number of units to be sampled (call this number n) o Using a random-number generator, generate n random integers between 1 and the total number of units in the population o Sample the units who numbers match those produced by the random number generator - How to take a random sample (Method 2): o Make the basic unit of sampling a group instead of a single individual o Take the average of the measurements of all the individuals within a unit (group) as the single independent observation for that unit (group) o Create a list of every unit (group) in the population of interest and give each unit (group) a number between 1 and the total population size o Decide on the number of units (groups) to be sampled (call this number n) o Using a random-number generator, generate n random integers between 1 and the total number of units (groups) in the population o Sample the units (groups) who numbers match those produced by the random number generator  Sample of Convenience - Sample of convenience- collection of individuals that are easily available to the researcher - Researcher must assume that a sample of convenience is unbiased and independent like a random sample, but there is no way to guarantee it - Sample of convenience may be biased because it doesn’t accurately represent that entire population of interest due to leaving out specific groups of individuals - Sample of convenience might also violate the assumption of independence if individuals in the sample are more similar to one another than individuals chosen randomly from the population  Volunteer Bias - Volunteer bias- bias resulting from a systematic difference between the pool of volunteer (the volunteer sample) and the population to which they belong - Volunteer bias arises
More Less

Related notes for BIOL 300

Log In


OR

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


OR

By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.


Submit