1st VIDEO
WHAT IS STATISTICS?
the science of collecting, organizing analysing, and interpreting data.
WHAT IS A STATISTICAL QUESTION?
is a question where you expect to get a variety of answers, and you are
interested in the distribution and tendency of those answers.
Survey questions – “how much do you weigh?”
Statistical questions – “how much do 6th grader weigh?”
2ND VIDEO
DATA - any observations that have been collected.
STATISTICS – collect, analyze, summarize, interpret, and draw conclusions
from data.
POPULATION – the complete set of elements being studied; group from which
a sample is drawn; exact population will depend on the scope of the study
ii. CONTINUOS DATA – Infinite numbers of possible
values (not countable) *usually a measure*
FOUR LEVELS OF MEASUREMENT
1. NOMINAL “categorizes” not “ordered”
Ex. Color, gender, ethnicity, country
2. ORDINAL can be ordered; differences are meaningless “ranked”
Ex. Rating scales, ranked orders
3. INTERVAL ordered; differences are meaningful; NO NATURAL ZERO
Ex. Time of day, year, IQ, Likert scales, Temperature
4. RATIO just like interval, but WITH NATURAL ZERO
Ex. Age, height, weight, rates
3RD VIDEO
MEASUREMENT SCALES
SCALE
SAMPLE – some subsets of a population; a small group of members selected
from a population to represent the population; subsets of population.
NOMINAL
TRUE
ZERO
NO
DISTANCE
ORDER
MEASURE
VALUE
NO
NO
COLOR
Red, green,
blue
Excellent,
very good,
good, fair,
poor
9:30 am,
noon, 10:00
pm
32 min. 7.5
hrs 4 days
CENSUS - collecting from every member of a population
ORDINAL
NO
NO
YES
RATING
INTERVAL
NO
YES
YES
TIME OF
DAY
RATIO
YES
YES
YES
DURATING
IF YOU TAKE A SAMPLE, IT MUST BE COLLECTED RANDOMLY
TYPES OF DATA
PARAMETER – a characteristic of a population
STATISTIC – a characteristic of a sample.
TWO TYPES OF DATA
1. QUALITATIVE (Categorical) – non-numerals
Ex. Color, gender, race, religion, ZIP codes
2. QUANTITATIVE – numerical
Ex. Height, weight, wages, distance, Temperature, Age, Time
a. TYPES OF QUANTITATIVE DATA
i. DISCRETE DATA - Countable or finite *usually a count*
FALSE ZERO = 0 °F
SIGNIFICANCE OF MEASUREMENT SCALES is that more powerful statistical
techniques are available for more powerful scales with ratio scale being the
most powerful and nominal scale being the least powerful.
4TH AND 5TH VIDEO
OBSERVATION – it measures specific traits but does not modify subjects
EXPERIMENT – apply a treatment and then measure the effect on the subjects.
SAMPLING - a method that allows researchers to infer information about a
population based on results from sample;
1. PROBABILITY SAMPLING – based on the fact that every member of a
population has a known and equal chance of being selected.
a. SIMPLE RANDOM SAMPLING – probability sample in which
every member of a study population has an equal chance of
selection.
2. NON PROBABILITY SAMPLING – involves non-random selection based
on convenience
RANDOM – each member of a population has an equal chance of being
selected in the sample.
OTHER SAMPLING TECHNQUES
1. SIMPLE RANDOM SAMPLING – a probability sample in which every
member of a study population has an equal chance of selection.
“METHOD OF CHNACE”.
2. CONVENIENCE SAMPLING – involves selecting samples based on
convenience; known as accidental sampling.
3. SNOWBALL SAMPLING – select samples and ask them to refer them to
refer you to others also called as “NETWORK SAMPLING”.
4. QUOTA SAMPLING – to take a much tailored sample that’s in
proportion to some characteristic or trait of a population. Used by
market researchers.
5. PURPOSIVE/ JUDGEMENTAL SAMPLING – selecting samples based on
his or her own judgement. Often used by media.
TWO ERRORS THAT MAY OCCUR
SIMPLE RANDOM SAMPLE – each group of size ‘n’ has an equal chance of
being selected.
1. NON-SAMPLING ERROR – math error
2. SAMPLING ERROR - difference in characteristics in a population.
FOUR COMMON SAMPLING TECHNIQUES
SAMPLING - a method that allows researchers to infer information about a
1. CONVENIENCE SAMPLE – use the results that are easy to get (NOT
population based on results from sample;
RANDOM)
2. SYSTEMATIC SAMPLING – put a population in some order and select
1. PROBABILITY SAMPLING
every “n th”; the sample is chosen using equal intervals or gaps.
a. SIMPLE RANDOM SAMPLING
3. STRATIFIED SAMPLE – break population into subgroups and based a
1. SIMPLE SAMPLING
characteristics, then sample each subgroup. ; formed by choosing a
2. SYSTEMATIC SAMPLING
simple random sample from each group.
3. CLUSTER SAMPLING
4. CLUSTER SAMPLING – divide population into clusters (regardless of
4. STRATIFIED SAMPLE
characteristics), randomly select a certain number of clusters, and then
2. NON PROBABILITY SAMPLING
collect data from the entire cluster; entire population is also classified
1. CONVENIENCE SAMPLING
into group; the researchers chooses entire groups or clusters to be a
2. SNOWBALL SAMPLING
part of the sample.
3. QUOTA SAMPLING
4. PURPOSIVE/ JUDGEMENTAL SAMPLING