Biostats Quiz 1

Observational study

a scientific study like experimental, but group being studied is uniform/all being treated the same way. Don’t need hypothesis testing or manipulation

Experimental Study

has a control and a changed thing in an experimental group. x variable must be manipulated.

hypothesis testing uses what kind of statistics

uses inferential statistics

statistics: descriptive v inferential

statistics: study of methods to describe and measure aspects of nature from samples. Used to test hypotheses and make inferences

Descriptive: describe and summarize data you actually collected (mean, median, variance, standard deviation)

Inferential: use sample data to make the conclusion about a population (estimate parameters, use t-tests, p values, etc)

precision/consistency

standard error, about consistency rather than accuracy. You want less deviation and measurements that are consistent with each other. Doesn’t require being close to the true value. You’re measurements are the same/close each time you do the experiment.

accuracy

(getting at the true value/state) how close your data is to the true population, how close your estimate of mean weight of crayfish is to the M =

order of scientific method (explain)

1. Ask questions: in present tense (does/do/is), “do students in bio and psych class differ in height?” “do antidepressants raise dopamine levels in mice?”

2. Form hypothesis: ex. Students in bio and psych class differ in height

3. Make an inference: and if/then statement, where you make a specific prediction about your sample. “If I measure 8 students in each class with a tape measure, then their heights will differ” “If I give a group of 10 mice oral antidepressants, their dopamine levels will be higher than the control of 10 mice”

4. Design the experiment: implement controls, design how you will collect data and analyze your statistics

5. Carry out the experiment/collect data: need to determine the modelling type to collect data correctly. x and y can be nominal (a name/category) or continous (a value/number)

6. Statistically analyze data

7. Make conclusions

statistics and how they are used to estimate populations

data collection and statistical analysis of a sample over time can give insight into parameters of a population

parameters v statistics

parameters describes population, statistics describes a sample

explanatory variable

also known as x or the independent variable. In an experimental study, x is manipulated, in observational, it is not/every simple individual in the sample is manipulated in the same way. (class)

response variable

the y or dependant variable. Is predicted/explained by x (height is predicted by class size)

qualitative modeling type

We need to know the modeling type to select the correct analysis/forms of x and y. Can be nominal (names categories) or ordinal (a name with an implied underlying order)

quantitative modeling type

can be continous (any value like height, speed, volume, the mean of discrete values etc.) or discrete (an integer)

3 sources of error when conducting experiments and hypothesis testing

(experiment or, chance, statistical)

• experimental: error in technique of using devices, qualitative inconsistencies (you see blue where others see purple), failing to use lottery method, interobserver bias (two people doing an experiment dont do exactly the same thing. You are not consistently following methodology

• chance/sampling: sample size is too small to represent the population.

• statistical: error in statistical analysis

ecs (e cs)

what is experimental error usually a result of? (One word)

Bias

lottery method. How can it be impacted (3 ways)

lottery method: best way of proper sampling. method of selecting individuals from a population to make a sample that is random and will give the most accurate values for the population.

• can be impacted by volunteer bias, sample of convenience (only sampling one team for example instead of randomly selecting all kinds of students), haphazard sampling (trying to be random and failing because of poor technique)

• don;t select samples all in the same proximity

• every individual in the pop. has an equal chance of being selected

• could use random # generator

qualities of representative samples

homogenous, adequate, unbiased

haphazard sampling

samples of convenience

3 reasons sampling goes wrong:

• When sampling goes wrong:

1. Haphazard sampling - a human, failed attempt to be random

2. Samples of convenience - only sample what is easily obtained, locally

3. Volunteer bias - eagerness of certain types of people to participate in studies

statistics generate data into….

probability

estimation

process of inferring an unknown quality of a target population using sample data

population

entire collection of individual units that a researcher is interested in

parameters

quantities describing populations (averages, proportions, variation)

variable/variability

characteristic that differs from individual to individual

null hypothesis

nothing is happening/no significance. Represents randomness. Is rejected by alternative hypothesis

partly sunny/ cloud cover

ordinal

collection of heights rounded to nearest whole number

continous

2 eggs in a birds nest

discrete

average number of eggs in birds nests in Hillsdale

continous

year people were born

discrete

months people are born

nominal

colors of the rainbow

nominal

species of trees

nominal

measuring if trees are small, medium, or large

ordinal

age

continous

star rating on yelp

ordinal

x̄

mean of a sample, a statistic

s

standard deviation, a statistic, describes sample

M (mu)

mean of a population, a paramter

σ

standard deviation of a population, a parameter

statistics describes…

a sample

parameter describes…

population

n

sample size

sigma squared

variance for the population (a mathematical measure for variability)

N

population size, is infinitely large

qualities of a representative sample: (3)

• homogenous: should be uniform, sample from 1 well defined population

• adequate: should have all the states of existence for variants that exist

• unbiased: individuals should be “in proportion” (if 5% of tree pop. are saplings, your sample should be ~5% saplings)

• hoes are ugly