Arithmetic and numerical computation |

Recognise and use expressions in decimal and standard form. | For example, converting data in standard form from a results table into decimal form in order to construct a pie chart. |

Use ratios, fractions and percentages. | For example, calculating the percentages of cases that fall into different categories in an observation study. |

Estimate results. | For example, commenting on the spread of scores for a set of data, which would require estimating the range. |

Handling data |

Use an appropriate number of significant figures. | For example, expressing a correlation coefficient to two or three significant figures. |

Find arithmetic means. | For example, calculating the means for two conditions using raw data from a class experiment. |

Construct and interpret frequency tables and diagrams, bar charts and histograms. | For example, selecting and sketching an appropriate form of data display for a given set of data. |

Understand simple probability. | For example, explaining the difference between the 0.05 and 0.01 levels of significance. |

Understand the principles of sampling as applied to scientific data. | For example, explaining how a random or stratified sample could be obtained from a target population. |

Understand the terms mean, median and mode. | For example, explaining the differences between the mean, median and mode and selecting which measure of central tendency is most appropriate for a given set of data. Calculate standard deviation. |

Use a scatter diagram to identify a correlation between two variables. | For example, plotting two variables from an investigation on a scatter diagram and identifying the pattern as a positive correlation, a negative correlation or no correlation. |

Use a statistical test. | For example, calculating a non-parametric test of differences using the data from a given experiment. |

Make order of magnitude calculations. | For example, estimating the mean test score for a large number of participants on the basis of the total overall score. |

Know the characteristics of normal and skewed distributions. | For example, being presented with a set of scores from an experiment and being asked to indicate the position of the mean (or median, or mode). |

Understand measures of dispersion, including standard deviation and range. | For example, explaining why the standard deviation might be a more useful measure of dispersion for a given set of scores, eg where there is an outlying score. |

Understand the differences between qualitative and quantitative data. | For example, explaining how a given qualitative measure (for example, an interview transcript) might be converted into quantitative data. |

Understand the difference between primary and secondary data. | For example, stating whether data collected by a researcher dealing directly with participants is primary or secondary data. |

Algebra |

Understand and use the symbols: =, <, <<, >>, >, ∝, ~. | For example, expressing the outcome of an inferential test in the conventional form by stating the level of significance at the 0.05 level or 0.01 level by using symbols appropriately. |

Graphs |

Translate information between graphical, numerical and algebraic forms. | For example, using a set of numerical data (a set of scores) from a record sheet to construct a bar graph. |

Plot two variables from experimental or other data. | For example, sketching a scatter diagram using two sets of data from a correlational investigation. |