A population is all the people, events or items which are of interest to be questioned, surveyed, or experimented on.
A census collects information from all members of a population.
Questionnaires are used to collect responses from people in a population by asking questions
Sampling involves selecting a smaller sub-group of people or objects from a population to test a theory or idea (a hypothesis) about the population
Two-way tables can be used to display information from a survey or experiment with two variables.
Bar charts are for displaying discrete qualitative data.
Pictograms make data easy to visualise, although they aren’t always as accurate as other charts. They use symbols to represent a set frequency of an item, and include a key to show how much of each quantity each symbol represents
Vertical line graphs are for discrete quantitative data
Discrete data – data that must take one of a set of certain values
Grouped discrete data – data points are given as values within a group.
Continuous data – numerical data which can take any value in a given range. Examples include age, height and weight
We use histograms to display continuous grouped data.
Cumulative frequency graphs show the total frequencies of grouped data at fixed points
Primary data is collected by the person carrying out the research. It could be collected by the researcher in a survey or questionnaire.
Secondary data is data collected by somebody else.
Box plots show the median and quartiles of a set of data
Range = Maximum Value - Minimum Value
Interquartile Range (IQR) = Q3 - Q1
The mode is the most common value or values.
The median is the middle value when the data is in numerical order
The mean of a set of data is the sum of all the values divided by the number of values
Mean = TotalnumberofvaluesSumofvalues
In a scatter graph, the closer the points are to representing a straight line, the more correlated they are said to be.
In a scatter graph, the closer the points are to representing a straight line, the more correlated they are said to be
Positive correlation = As the variable on the x-axis increases, the variable on the y-axis increases. The line of best fit has a positive gradient.
No correlation = There is no relationship between the two sets of data. There is no identifiable line of best fit.
Negative correlation = As the variable on the x-axis increases, the variable on the y-axis decreases. The line of best fit has a negative gradient.