Brainscape's Knowledge GenomeTM


Top Flashcards (Certified)
All Flashcards

Statistical Biology > Basic Concepts > Flashcards

Basic Concepts Flashcards

(54 cards)

Start Studying
1
Q

deals with the scientific method of collecting, organizing, summarizing, presenting, and analyzing data, as well as drawing valid conclusions and making reasonable decision on the bases of this analysis

A

Statistics

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

What is the basic concern in the study of statistics

A

presentation and interpretation of chance outcomes occurred in a planned or scientific investigation

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

values that the variables can assume

A

Data

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

Values whose values are determined by chance

A

Random variables

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

Two types of variables

A
  1. Qualitative variables
  2. Quantitative variables
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

words or codes that represent a class or category

A

Qualitative variables

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

numbers that represent an amount or a count

A

Quantitative variables

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

Classification of Quantitative variables

A
  1. Discrete variables
  2. Continuous variables
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q
  • can be assigned values as 0, 1, 2, 3, …
  • said to be countable
A

Discrete variables

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

can assume all values between any two specific values like 0.5, 1.2, etc.

A

Continuous variables

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

Ex. of continuous variable

A

length of a wire

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

Ex. of discrete variable

A

number of persons in a room

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

procedures used in the collection, presentation, analysis, and interpretation of data

A

Statistical Methods

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

Two (2) major areas where statistical methods may belong to

A
  1. Descriptive statistics
  2. Statistical inference
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

comprises those methods concerned with COLLECTING and DESCRIBING a set of data so as to yield MEANINGFUL information

A

Descriptive Statistics

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

Descriptive statistics provides information only about the __ __ and in no way draws inferences or conclusions concerning a larger set of data.

A

collected data

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

What are constructed under Descriptive Statistics

A
  • tables
  • graphs
  • charts
  • other relevant computations
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

comprises those methods concerned with the ANALYSIS of a subset of data leading to PREDICTIONS or INFERENCES about the entire set of data

A

Statistical Inference

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

consists of methods that are used to INFER CHARACTERICSTICS of a population from observations on sample or formulate general laws on the basis of repeated observations

A

Inferential Statistics

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

Two (2) types of problems statistical inference is concerned with

A
  1. Estimation of population parameters
  2. Tests of hypotheses
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

consists of the totality of the observations with which we are concerned

A

Populations

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

subset of a population

23
Q

any numerical value describing a characteristic of a population

24
Q

any numerical value describing a characteristic of a sample

25
Level of Measurement
1. Nominal level 2. Ordinal level 3. Interval level 4. Ratio level
26
- characterized by data that consists names, labels, or categories only - data cannot be arranged in an ordering scheme - no criterion as to which values can be identified as greater than or less than other values
Nominal level
27
- involves data that may be arranged in some order - differences between data values either cannot be determined or meaningless - means in order
Ordinal level
28
- can determine meaningful amounts of differences between data - data may lack an inherent zero starting point - has values of equal intervals that mean something
Interval level
29
- modified to include inherent zero starting point - differences and ratios of data are meaningful - highest level of measurement - zero on the scale means it does not exist
Ratio level
30
Example of Nominal level
- gender - hair color
31
Example of Ordinal level
- high school class ranking - social economic class - Likert scale
32
Example of Interval level
- Celsius temperature - IQ - Time on a clock with hands
33
Example of Ratio level
- weight - height - ruler measurements
34
Classifications of Statistical Techniques
1. univariate 2. bivariate 3. multivariate
35
technique applies to a single variable
univariate
36
technique applies to two variables
bivariate
37
technique applies to more than two variables
multivariate
38
technique involves estimation of population parameters and test hypothesis
inferential
39
Two (2) basic types of sampling procedures
1. Nonprobability sampling 2. Probability sampling
40
there is no way of estimating the probability that each individual or element will be included in the sample
Nonprobability sampling
41
each individual has an equal chance of becoming a part of the sample
Probability sampling
42
Examples of nonprobability sampling
1. accidental or incidental samples 2. quota sampling 3. purposive sampling
43
may constitute the entire sample
accidental or incidental samples
44
proportions of various subgroups in the population are determined and the sample is drawn to have the same percentages in it
quota sampling
45
researchers rely on their own judgment when choosing members of the population to participate in their surveys
purposive sampling
46
Examples of probability sampling
1. simple random sampling 2. systematic sampling 3. cluster sampling 4. stratified random sampling
47
each individual in the population has an equal chance of being drawn into the sample
simple random sampling
48
selects every kth element in the population for the sample, with the starting point to be determined at random from the first k elements
systemic sampling
49
selects a sample containing either all, or a random selection, of the elements from clusters that have themselves been selected randomly from the population
cluster sampling
50
selects simple random samples from mutually exclusive subpopulations, or strata, of the population
stratified random sampling
51
characteristic or entity that can assume different values
variable
52
What is the primary concern of statistical description
variation in values for a given characteristic
53
total set of values for a particular characteristic
distribution of the variable
54
variable that can theoretically assume any value between two given value
continuous

Statistical Biology (2 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions