sampling methods (yr1 applied ch1)

define population, census and sample

population: entire group of items subject to statistical study

census: when all members of the population take part in statistical study

sample: a subset of the population selected for analysis.

define sampling frame and bias

sampling frame: a list of all the members of the population from which a sample is drawn.

bias: a systematic error that leads to inaccurate conclusions about the population.

adv and disadv of census

adv: representative of whole population, completely accurate

disadv: time consuming + expensive. not good for when testing an item will destroy it, bad for large populations

adv and disadv of samples

adv: less time consuming and expensive to draw for smaller populations. less data to process

disadv: not representative of entire population, potential for bias during sample selection

what is random sampling

sampling methods where every item in population has equal chance of being selected.

systematic, stratified and simple random sampling

explain simple random sampling and give adv and disadv

get sampling frame of population ready.

use random number geenrator to select the sample no. of items from sampling frame

ADV: simple to carry out, cheap and easy for small populatuons

DISADV: needs sampling frame. time consuming for larger populatuons

explain systematic sampling and give adv and disadv

do population / sample to give value (lets call this x).

user random no. generator to generate a value from 1 to x inclusive as starting point.

systematically choose items x apart from eachother until sampel size is covered.

ADV: suitable for larger populations. populatuon will be evenly sampled

DISADV: can be biased - may not capture hidden patterns

explain stratified sampling and give adv and disadv

population divided into naturally occuring subgroups (strata).

(strata1 size / population) * total sample size = sample size for that strata.

then follow simple random sampling for each strata.

ADV: groups are equally represented, minimising bias.

DISADV: needs sampling frame. splitting population into carefully defined strata can be difficult.

what is non-random sampling. explain 2 of them

sampling where everyone doesnt have equal chance of being selected

quota sampling - population split into strata. select a pre-determined sample from each strata. researchers choice who to pick.

opportunity sampling - select first items you see of target population until sample size is covered.

give adv and disadv of quota sampling

adv: doesnt need sampling frame. quick easy for small population. quite representative bc of strata.

disadv: can be biased, non-random, increasing scope of study can b time-consuming

give adv and disadv of opportunity sampling

adv: really easy to carry out, doesnt need sampling frame

disadv: can be very biased as depenedent on researched. not very representative.

daily total rainfall info

daily mean cloud cover info

daily total sunshine info

wind stuff info

dtr: less than 0.05mm is shown as tr (trace)

dtcc: measured in oktas, how many 1/8’s of the sky is covered in clouds

dts: measured as a tenth of an hour (e.g. 1.4, 4.7)

wind related stuff is measured in knots