Statistics

Question 1

Sample of convenience

Answer 1

A collection of individuals that happen to be available at the time

Question 2

Sampling error

Answer 2

The chance difference between an estimate and the population parameter being estimated

Question 3

Bias

Answer 3

A systematic discrepancy (tending in a certain direction) between an estimate and the true population

Question 4

Error

Answer 4

A random difference (not tending in any direction) between an estimate and the true population characteristic

Question 5

(Larger, normal, small) samples on average will have smaller sampling error

Answer 5

Larger

Question 6

Increase the number of individuals in your sample

Answer 6

Decrease sampling error

Question 7

Ensure random sampling

Answer 7

Reduce sampling bias

Question 8

Variance

Answer 8

Average squared deviation from the mean

Question 9

Coefficient of variation

Answer 9

Expresses how big the standard deviation is in relation to the mean

Question 10

Variation in sample means decreases with ____

Answer 10

Increased sample size

Question 11

Standard error

Answer 11

The standard deviation of a sampling distribution (predicts the sampling error of the estimate)

The standard error of an estimate of a mean is the standard deviation of the distribution of sample means.

Question 12

95% Confidence Interval

Answer 12

Provides a plausible range of the parameter (95% of all 95% confidence intervals calculated from samples will include the population mean)

Question 13

Pseudoreplication

Answer 13

Error that occurs when individual measurements are not independent but are treated as though they are

Question 14

Test statistic

Answer 14

A number calculated to represent the match between a set of data and the null hypothesis

Question 15

P-value

Answer 15

The probability of getting the data or something more unusual if the null hypothesis were true

Question 16

Type I Error

Answer 16

• Rejecting a true null hypothesis
• Pr[Type I Error] = a
• Does not depend on sample size

Question 17

Type II Error

Answer 17

• Not rejecting a false null hypothesis
• Pr[Type II Error] = B
• B lowers with larger sample sizes
• The smaller the B the more power a test has

Question 18

Power

Answer 18

The ability of a test to reject a false null (Power = 1 - B)

Question 19

Poisson distribution

Answer 19

Describes the probability that a certain number of events occur in a block of time or space, when those events happen independently of eqch other and occur with equal probability at every point in space/time

Question 20

Central limit theorem

Answer 20

The sum or mean of a large number of measurements randomly sampled from any population is approximately normally distributed

Question 21

Null distribution for a test statistic

Answer 21

The probability distribution of alternative outcomes when a random sample is taken from a hypothetical population in which the null hypothesis is true

Question 22

Paired design

Answer 22

• Data from two groups are paired
• Each member of a pair shared much in common except for the tested categorical variable
• Accounts for extraneous variation
• Mean of the differences

Question 23

Transformation require:

Answer 23

1) Same transformation applied to each individual/group
2) One-to-one correspondence with original value (no ambiguity)
3) Monotonic (order stays the same)

Question 24

Goals of experiments

Answer 24

1) Eliminate bias
2) Reduce sampling error

Question 25

Features that reduce bias

Answer 25

1) Controls
2) Randoom assignment of treatments (averages the effects of confounding variables)
3) Blinding/anonymizing

Question 26

How to reduce sampling error

Answer 26

Increase signal to noise ratio

Lower “noise” by increasing sample size and reducing variation within groups (all other factors as equal as possible)

Question 27

Design features to reduce sampling error

Answer 27

1) Replication: carry out study on multiple independent objects
2) Balance: nearly equal sample sizes in each treatment
3) Blocking: Grouping experimental units and applying different treatments within each group (accounts for extraneous variables)
4) Extreme treatments: stronger treatments

Question 28

Matching

Answer 28

Pair individuals in treatment group with control individuals with similar values for confounding variables (reduces bias by limiting confounding and reduces sampling error analogous to blocking)

Question 29

r^2

Answer 29

Describes the proportion of variation in one variable that can be predicted from the other variable (the proportion of variance in Y that can be predicted by the regression line)

Question 30

Attenuation

Answer 30

The estimated correlation will be lower if X or Y are estimated with error