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Statistical Biology > Basic Concepts > Flashcards

Basic Concepts Flashcards

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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

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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

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3
Q

values that the variables can assume

A

Data

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4
Q

Values whose values are determined by chance

A

Random variables

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5
Q

Two types of variables

A
  1. Qualitative variables
  2. Quantitative variables
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6
Q

words or codes that represent a class or category

A

Qualitative variables

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7
Q

numbers that represent an amount or a count

A

Quantitative variables

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8
Q

Classification of Quantitative variables

A
  1. Discrete variables
  2. Continuous variables
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9
Q
  • can be assigned values as 0, 1, 2, 3, …
  • said to be countable
A

Discrete variables

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10
Q

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

A

Continuous variables

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11
Q

Ex. of continuous variable

A

length of a wire

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12
Q

Ex. of discrete variable

A

number of persons in a room

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13
Q

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

A

Statistical Methods

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14
Q

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

A
  1. Descriptive statistics
  2. Statistical inference
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15
Q

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

A

Descriptive Statistics

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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

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17
Q

What are constructed under Descriptive Statistics

A
  • tables
  • graphs
  • charts
  • other relevant computations
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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

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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

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20
Q

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

A
  1. Estimation of population parameters
  2. Tests of hypotheses
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21
Q

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

A

Populations

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22
Q

subset of a population

A

Sample

23
Q

any numerical value describing a characteristic of a population

A

Parameter

24
Q

any numerical value describing a characteristic of a sample

A

Statistic

25
Q

Level of Measurement

A
  1. Nominal level
  2. Ordinal level
  3. Interval level
  4. Ratio level
26
Q
  • 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
A

Nominal level

27
Q
  • involves data that may be arranged in some order
  • differences between data values either cannot be determined or meaningless
  • means in order
A

Ordinal level

28
Q
  • can determine meaningful amounts of differences between data
  • data may lack an inherent zero starting point
  • has values of equal intervals that mean something
A

Interval level

29
Q
  • 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
A

Ratio level

30
Q

Example of Nominal level

A
  • gender
  • hair color
31
Q

Example of Ordinal level

A
  • high school class ranking
  • social economic class
  • Likert scale
32
Q

Example of Interval level

A
  • Celsius temperature
  • IQ
  • Time on a clock with hands
33
Q

Example of Ratio level

A
  • weight
  • height
  • ruler measurements
34
Q

Classifications of Statistical Techniques

A
  1. univariate
  2. bivariate
  3. multivariate
35
Q

technique applies to a single variable

A

univariate

36
Q

technique applies to two variables

A

bivariate

37
Q

technique applies to more than two variables

A

multivariate

38
Q

technique involves estimation of population parameters and test hypothesis

A

inferential

39
Q

Two (2) basic types of sampling procedures

A
  1. Nonprobability sampling
  2. Probability sampling
40
Q

there is no way of estimating the probability that each individual or element will be included in the sample

A

Nonprobability sampling

41
Q

each individual has an equal chance of becoming a part of the sample

A

Probability sampling

42
Q

Examples of nonprobability sampling

A
  1. accidental or incidental samples
  2. quota sampling
  3. purposive sampling
43
Q

may constitute the entire sample

A

accidental or incidental samples

44
Q

proportions of various subgroups in the population are determined and the sample is drawn to have the same percentages in it

A

quota sampling

45
Q

researchers rely on their own judgment when choosing members of the population to participate in their surveys

A

purposive sampling

46
Q

Examples of probability sampling

A
  1. simple random sampling
  2. systematic sampling
  3. cluster sampling
  4. stratified random sampling
47
Q

each individual in the population has an equal chance of being drawn into the sample

A

simple random sampling

48
Q

selects every kth element in the population for the sample, with the starting point to be determined at random from the first k elements

A

systemic sampling

49
Q

selects a sample containing either all, or a random selection, of the elements from clusters that have themselves been selected randomly from the population

A

cluster sampling

50
Q

selects simple random samples from mutually exclusive subpopulations, or strata, of the population

A

stratified random sampling

51
Q

characteristic or entity that can assume different values

A

variable

52
Q

What is the primary concern of statistical description

A

variation in values for a given characteristic

53
Q

total set of values for a particular characteristic

A

distribution of the variable

54
Q

variable that can theoretically assume any value between two given value

A

continuous

Statistical Biology (2 decks)

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