Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

BIOL 315 > Midterm > Flashcards

Midterm Flashcards

1
Q

what is population

A

set of all “subjects” relevant to scientific hypothesis

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

what are parameters

A

quantities describing a population

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

what are variables

A

characteristics that differ among individuals

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

describe two categorical variables

A

nominal and ordinal

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

describe two numerical variables

A

interval and ratio scaled

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

what is nominal scale

A

categorically discrete (species, sex, diet)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

what is ordinal

A

ordering (small, medium, large)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

what is interval scaled

A

intervals, arbitrary 0 (celsius, years BC)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

what is ratio scaled

A

natural zero point (mass, abundance, duration)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

what is a sample

A

subset of “subjects” selected from statistical population that are actually examined during particular study

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

what are sample statistics

A

calculated from collected sample to estimate population parameter

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

what is random sampling

A

equal chance of being selected, independent

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

what is haphazard sampling

A

does not follow systematic way of collecting samples

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

what are descriptive statistics

A

quantities that capture important features of frequency distribution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

what is frequency distribution

A

describes the # of times (frequency) each value of a variable occurs in a sample

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

what are 3 measures of location

A

mean (average), median (middle value), mode (commonly occurring value)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

what is measures of spread

A

description of variation around the typical individual

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

what is a residual

A

difference btw observation and mean

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

what are degrees of freedom

A

describe the # of values in a calculation that are free to vary

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

what is a positive skew

A

tail on right

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

what is a negative skew

A

tail on left

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

what is estimation

A

process of inferring a population parameter from sample data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

what is sampling distribution

A

probability distribution of all values for an estimate that we might have obtained when we sampled the population (pot the distribution of means calculated from sampling the population)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

what is bias

A

difference between the mean of the sampling distribution of a sample statistic, and the true population value

25
Q

what is standard error

A

standard deviation of the sampling distribution of a sample statistic measures sampling error (standard deviation (s) of sample/square root of sample size (n))

26
Q

what is a 95% confidence interval

A

if you sampled repeated, 95% of the time the resulting interval would contain the true population

27
Q

what is probability

A

likelihood of hypothesis given data

28
Q

what is hypothesis testing

A

inferring whether statistical claims about the parameters (statistical hypotheses) are true or not

29
Q

assumption of null hypothesis

A

any variation we see is due to sampling error alone

30
Q

what is null hypothesis

A

specific statement about population parameter made for purpose of argument

31
Q

what is alternate hypothesis

A

represents all other possible parameter values except that is stated in the null hypothesis (mutually exclusive and exhaustive)

32
Q

what is the null distribution

A

probability distribution of test statistic values when a random sample is taken from a hypothetical population for which the null hypothesis is true

33
Q

how to determine p value

A

compare test statistic value to the null distribution and determine probability of obtaining the data

34
Q

what is significance value

A

probability used as a criterion for rejecting the null hypothesis

35
Q

P-value = significance level

A

reject null

36
Q

p-value>significance level

A

fail to reject

37
Q

example of good biological conclusion

A

The probability of winning a wrestling match differs significantly from 0.50 when the athlete is wearing a red shirt (name of test, test statistic, n (or df), P

38
Q

what is a critical value

A

boundary btw values that support null and those that lead to reject null (if test statistic more extreme you reject null)

39
Q

at critical values, area under tails

A

one tailed - 5%

two tailed - 2.5%

40
Q

what is type 1 error

A

reject true null (false positive)

41
Q

what is type 2 error

A

failing to reject false null (false negative)

42
Q

what does power depend on

A

what alternative hypothesis is true

type 1 error rate sample size (precision)

43
Q

what is power

A

the probability we will reject a false null

44
Q

what is a binomial distribution

A

discrete distribution that arises from the outcome of a number of “Bernoulli Trials”

45
Q

what are 3 characteristics of bernoulli trials

A
  • only 2 possible outcomes
  • outcome of each bernoulli trial is independent
  • probability of success is identical for all trials
46
Q

what are 3 characteristics of binomial distribution

A
  • distribution is completely determined by the parameters p (probability of success) and n (# of trials)
  • mean of binomial distribution is mean = np
  • variance of binomial distribution ??????
47
Q

what is a poisson distribution

A

distribution describes the frequency distribution of events that occur rarely and randomly. success are described in blocks of time or space (rather than probability for a given trial)

48
Q

what are the 4 conditions of poisson

A
  • probability of 2 or more occurrences in a single sample subdivision is negligibly small
  • probability of 1 occurrence in a sample subdivision is proportional to the size of the subdivisions (in time or space)
  • outcome in 1 subdivision of the sample unit is independent of the outcome in all other subdivisions
  • probability of an occurrence is identical for all sample subdivisions
49
Q

what are two analysis of frequency tests and what do they analyze

A

goodness of fit
contingency analysis
analyze frequency of observations in different categories (compares observed freq of observations in different categories to expected freq under some observation; if they are sufficiently different, reject null

50
Q

what are two tests for the analysis of categorical data

A

chi squared test

log likelihood ratio test (G test)

51
Q

for chi-squared what do Oi Ei and k mean

A

Oi-observed frequency (# of observations) in category i
Ei-expected frequency
k-total number of categories

52
Q

what steps do both tests involve

A

-calculate expected freq for each category
calculate test stat based on dissimilarity btw observed and expected
-compare test stat to chi squared distribution w/ appropriate df

53
Q

what is goodness of fit

A

compares frequencies/counts to discrete probability distribution
tests whether observed distribution of counts across classes consistent w/ what you’d expect based on hypothesized probability distribution
asks if hypothesized prob. is a good “fit” to observed data

54
Q

what is the null hypothesis for goodness of fit

A

proportion of observation in a category are equal to expected proportions

55
Q

how does the contingency test differ from goodness of fit

A

asking whether two categorical variables are associated with each other (variable is “contingent” or “dependent on” the other)

56
Q

what are extrinsic expectations/hypotheses

A

are when expected freq are derived from info other than the data analyzing

57
Q

what are intrinsic expectations/hypotheses

A

when expected freq are derived from the data you are analyzing (no info asumed prior to study)

58
Q

what is a contingency table

A

analyze whether which row observations into is contingent on which column it falls into and vice versa

BIOL 315 (1 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions