BIOM FINAL CARDS - pt 1

our aspects of a good sample

Random; independent; unbiased; representative

Population

Entire group of interest

Sample

Subset of the population

Experimental unit

Object measured

Variable

Measured characteristic

Data

All collected values

Statistic

Sample summary value

Parameter

Population summary value

Sampling error

Difference between sample result and population truth

Nominal variable

Qualitative with no order

Ordinal variable

Qualitative with order

Discrete variable

Quantitative whole numbers

Continuous variable

Quantitative decimals

Univariate data graph (qualitative)

Bar or circle graph

Univariate data graph (quantitative)

Histogram or boxplot

Bivariate qualitative data

Contingency table

Bivariate quantitative data

Scatterplot

One qualitative and one quantitative variable

Side-by-side boxplots

Three types of probability

Empirical

Empirical probability

Based on repeated trials

Theoretical probability

Based on reasoning

Subjective probability

Based on judgment

Ways to show probability

Venn diagram

Independent events test

P(A) = P(A|B) or P(A and B) = P(A)P(B)

Mutually exclusive test

P(A and B) = 0

Binomial probability problem

Fixed trials

Normal probability problem

Uses z-scores and normal curve

Standard deviation

Measures spread of individual data

Standard error

Measures spread of sample means

Standard error represents

Variability of sample means

Probability of a mean

Uses sampling distribution

Correct decision

Appropriate conclusion about null hypothesis

Type I error meaning

False positive

Type II error meaning

False negative

Four steps of hypothesis testing

State hypotheses

Nonparametric tests

Use ranks or signs instead of raw values

Sign test

Uses direction of differences only

Rank tests

Use ordered values to retain more information

Test selection

Depends on variables

Tail direction

Determined by wording of alternative hypothesis