Statistics (sampling)

everyone in an investigation

representative of the population

a survey of the entire population

a list of all members of the population

school register

a distinct element of the population that can be picked to form a sample

-hat method

- random number generator

-random number table

the size of each group in the sample is in proportion to the sizes of the group in the population

split the population into groups and use the formula (stratified sample =strata divided by total X sample size) to calculate the sample size for each group and use random sampling to select members from each group

sample is in proportion to population, so sample represents the population fairly

best used for populations with groups of unequal sizes

time consuming

choosing items in the population at regular intervals

Population is divided into natural groups, groups are chosen randomly and every member of the group is sampled

when surveying lots of different towns in a country.

population is grouped by characteristics and a fixed amount is sampled from each group

using the people/items available at the time, e.g.": interviewing the first 10 ppl you see

when the reasercher uses their own judgement to select a sample that they think represents a population.

used to estimate the size of large/moving populations where it’d be impossible to count the entire population

M/N = m/n —> first capture/total (N) = tagged/second capture

1- take a sample of the population

2- mark each item

3- put items back into the population and ensure theyre thouroughly mixed

4- take a second sample and count how many of your sample are marked

5- the proportion of marked items in your new sample should be the same as the proportion of marked items from the population in your first sample

used when a researcher in how changes in one variable affect another.

– The variable that is changed.

The variable that is measured.

Variables you are not interested in but that could affect the result of

your experiment.

Researcher has full control over variables. Conducted in a lab or similar

environment.

▪ Explanatory variable - age

▪ Response variable - reaction time.

▪ Extraneous variables - gender, health condition, fitness level etc.

Carried out in the everyday environment. Researcher has some control over the variables. They set up the situation and controls the explanatory variable but has less control over extraneous variables.

▪ Explanatory variable – method of revision

▪ Response variable – results in exam

▪ Extraneous variables – amount of revision pupils does, ability of pupils.

Carried out in the everyday environment. Researcher has no/very little control over the variables. Explanatory variables are not changed but instead researchers look at something that already exists in the world and how it affects other things.

▪ Explanatory variable – level of education

▪ Response variable – income

▪ Extraneous variables – IQ, other skills people may have, personal circumstances

Simulation – A way to model random events using random numbers and previously collected data. These could be used to help you predict what could actually happen in real life.Easier and cheaper than actually collecting the data.

1. Choose a suitable method for getting random numbers – dice, calculator, random number tables.

2. Assign numbers to the data.

3. Generate the random numbers.

4. Match the random numbers to your outcomes.