Brainscape's Knowledge GenomeTM

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


See full index

Data Science for Biologists > Exam Revision > Flashcards

Exam Revision Flashcards

You may prefer our related Brainscape-certified flashcards:
Biology 101 flashcards
1
Q

What is the third assumption of ANOVA?

A

The variance is the same in all k populations.

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

When is it appropriate to use a contingency table?

A

When the response variable is categorical and the explanatory variable is categorical.

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

What are the four components of an effective figure caption?

A
  1. title
  2. description of the techniques or methods used
  3. statement of the main results
  4. explanation of symbols, error bars or legends
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

The effects of pesticide and predator presence on tadpole survival were examined. 16 tubs were each filled with four tadpoles. Four tubs were randomly assigned to each combination of treatments (predator & pesticide, predator no pesticide, no predator & no pesticide, no predator and pesticide). What are the independent sampling units?

A

tubs

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

What is the second assumption of ANOVA?

A

The variable is normally distributed in each of the k populations.

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

When should you use a Tukey-Kramer test?

A

To test all pairs of means to find out which groups stand apart, following an ANOVA.

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

When should you use ANOVA?

A

To compare more than two means to each other. The response variable is numerical and the explanatory variable is categorical.

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

Define ‘Type II error’.

A

A Type II error is failing to reject a false null hypothesis.

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

What are the four goals of graphing?

A
  1. Show the data.
  2. Make patterns in the data easy to see.
  3. Represent magnitudes honestly.
  4. Draw graphical elements clearly.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Define ‘P-value’

A

The P-value is the probability of obtaining the data (or data showing as great or greater difference from the null hypothesis) if the null hypothesis were true.

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

When is it appropriate to use a mosaic plot?

A

When the response variable is categorical and the explanatory variable is categorical.

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

Does randomisation eliminate bias or reduce sampling error?

A

eliminate bias

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

Define ‘Type I error’.

A

A Type I error is rejecting a true null hypothesis. The significance level, alpha, sets the probability of committing a Type I error.

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

When is it appropriate to use a strip chart?

A

When the response variable is numerical and the explanatory variable is categorical.

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

What does a Q-Q plot tell us?

A

The Q-Q plot shows whether Y is normally distributed across X.

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

Define ‘variable’.

A

A variable is a characteristic that differs among individuals.

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

What is the third assumption of linear regression?

A

The variance of Y values is equal at all values of X.

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

When should you use a chi-squared contingency test?

A

To compare frequencies or proportions to each other. The response variable is categorical and the explanatory variable is categorical.

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

When should you use a two sample t-test?

A

To compare two means to each other. The response variable is numerical and the explanatory variable is categorical.

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

Does blinding eliminate bias or reduce sampling error?

A

eliminate bias

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

What is the formula when planning for power?

A

n = 16(SD/D)^2

22
Q

When should you use a chi-squared goodness of fit test?

A

To compare frequencies or proportions to null values. The response variable is categorical.

23
Q

When should you use linear regression?

A

To compare slopes or trends to a null value. The response variable is numerical and the explanatory variable is numerical.

24
Q

When is it appropriate to use multiple histograms?

A

When the response variable is numerical and the explanatory variable is categorical.

25
Q

Define ‘random sample’.

A

A random sample is a sample in which each member of a population has an equal and independent chance of being selected.

26
Q

What is the first assumption of linear regression?

A

Y is linearly related to X.

27
Q

Does balance eliminate bias or reduce sampling error?

A

reduce sampling error

28
Q

What is the fourth assumption of linear regression?

A

Values of Y are randomly sampled at all values of X.

29
Q

What does a residual plot tell us?

A

The residual plot shows whether the relationship between Y and X is linear.

30
Q

When is it appropriate to use a scatter plot?

A

When the response variable is numerical and the explanatory variable is numerical.

31
Q

When is it appropriate to use a grouped bar graph?

A

When the response variable is categorical and the explanatory variable is categorical.

32
Q

Define ‘data’.

A

Data are the measurements of one or more variables made on a sample of individuals.

33
Q

Define ‘replication’.

A

Replication is the application of every treatment to multiple, independent experimental units.

34
Q

Define ‘sampling error’.

A

Sampling error is the chance difference between an estimate and the population parameter being estimated caused by sampling.

35
Q

When should you use a paired t-test?

A

To compare two paired means to each other. The response variable is numerical and the explanatory variable is categorical.

36
Q

Define ‘interaction’.

A

An interaction between two or more explanatory variables means that the effect of one variable depends upon the state of the other variable.

37
Q

Define ‘observational study’.

A

An observational study is a study in which the assignments of treatments is not made by the researcher.

38
Q

When should you use a one sample t-test?

A

To compare one mean to a null value. The response variable is numerical.

39
Q

Define ‘bias’.

A

Bias is a systematic discrepancy between the estimates we would obtain, if we could sample a population again and again, and the true population characteristic.

40
Q

What is the second assumption of linear regression?

A

The distribution of Y values is normal at all values of X.

41
Q

What is the first assumption of ANOVA?

A

The measurements in every group represent a random sample from the corresponding population.

42
Q

Do simultaneous control groups eliminate bias or reduce sampling error?

A

eliminate bias

43
Q

Define ‘experimental study’.

A

An experimental study is a study in which the researcher assigns treatments randomly to individuals.

44
Q

Does consistency of conditions eliminate bias or reduce sampling error?

A

reduce sampling error

45
Q

Define ‘factorial experiment’.

A

A factorial experiment investigates all treatment combinations of two or more variables. A factorial design can measure interactions between treatment variables.

46
Q

Which feature of experimental design cannot be included in observational studies?

A

randomisation

47
Q

Does replication eliminate bias or reduce sampling error?

A

reduce sampling error

48
Q

Does blocking eliminate bias or reduce sampling error?

A

reduce sampling error

49
Q

Define ‘parameter’.

A

A parameter is a quantity describing a population.

50
Q

When is it appropriate to use a box plot?

A

When the response variable is numerical and the explanatory variable is categorical.

Data Science for Biologists (1 decks)

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