Statistics gcse

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36 Terms

1
  • qualitative data

  • - data that can only be written in words, not numbers, for example, the colours of cars in a car park

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2
  • quantitative data

  • - data that can be written in numbers, for example, the heights of children

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3
  • discrete data (type of quantitative data)

  • numerical data that cannot be shown in decimals, for example, the number of children in a classroom

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4
  • continuous data (type of quantative data)

  • numerical data that can be shown in decimals, for example, the weights of 10 babies

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5
  • primary data

  • data that has been collected from the original source for a specific purpose, for example, if a school wanted to know what their students thought of the school canteen service they would question the pupils directly

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6

Secondary data

  • - data that is not originally collected by a group for a specific purpose, for example, finding out the average cost of cars in a car park by using national statistics

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7

Systematic sampling

  1. collecting data in an ordered or regular way, eg every 5 metres or every fifth person.

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8

stratified sampling

  1. dividing sampling into groups, eg three sites from each section of coastline, or five people from each age range. It is possible to combine stratified sampling with random and systematic sampling.

  • ļ»æļ»æStratified random sampling - random samples are taken from within certain categories.

  • ļ»æļ»æStratified systematic sampling - regular samples are taken from within certain categories.

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9

Random sampling

  1. selecting a person to interview or site to measure, at random. Random sampling is unbiased as particular people or places are not specifically selected.

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10

Things to consider when collecting data

  • The sample size

  • whether itā€™s representative

  • Whether itā€™s biased

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11

How to avoid bias

Sampling method or by increasing the sample size

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12

How to collect data

Questionnaires

Interviews

Observations

Surveys

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13

Mean

Total number of all values divided by the number of values

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14

Median

The middle value

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15

Mode

The most common value

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16

Range

The difference between the highest and lowest value

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17

Interquartile range

Upper quartile - lower quartile

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18

Ways to represent quantitive data

Table, bar chart, scatter graph etc

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19

Representing qualitative data

Use a pie chart etc

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20

Examples of bias in surveys

Voluntary response sampling- People have chosen to take the survey

Response bias - The wording makes people not want to answer truthfully

Convenience sampling - the first people to show up

Non response - people donā€™t reply so their pov is missed

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21

Non random sampling

Voluntary: asking people to do a survey

Convince: Sampling the first people to show up

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22

Pros of grouped data

  • Makes the data easier to read and understand

  • Easier to spot patterns and compare data

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23

Cons of grouped data

  • Lose accuracy of the data as you no longer know the exact values

  • Calculations made from these will only be an estimate

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24

Laboratory experiments

  • Researcher has full control of the variables

  • Easy to replicate

  • People may react differently to normal under test conditions

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25

Field experiments

  • Carried out in an everyday environment

  • Researcher has some control of the variables

  • Not easy to replicate and itā€™s hard to control extraneous variables

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Natural experiments

  • Carried out in the everyday environment

  • Researcher has no/little control of the variable

  • Reflects real life behaviour

  • Low validity due to little control of the variables and itā€™s difficult to replicate

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27

Frequency density equation

Frequency density = Frequency/ class width

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28

Types of misleading diagrams

  • Pictograms - same symbol and size need to be used for all the diagrams and a key is needed

  • 3d charts - Distort part of the diagram making it difficult to read the values

  • Colours - Some colours make part of the diagram stand out more, making it seem more important

  • Lines drawn too thick make it difficult to read information

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29

Axes and scales that can be misleading

  • A scale that does not start at zero

  • Missing values on the scales

  • Axes that are unevenly scales

  • Axes that are not labelled

  • Not using a key

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30

Bivariate

Involves measuring two variables

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31

Multivariate

Made up of two or more variables

eg: comparing height, weight, age and shoe size together

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cluster sampling

The population is put into random groups, one group is randomly selected and everyone in the group is asked

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33

Census

Collects information from all members of a population

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34

Lower class boundary

The smallest number in the class

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35

Pilot study

A pilot study is a small scale study that is conducted to inform, predict and direct an intended future scale study

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36

Opinion scale

An opinion scale lets people express their feelings on a numbered scale

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