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neurologyYou need to know

• Quantitative data in PE is numerical data that can be counted, measured or calculated.
• Quantitative data in PE answers questions such as how many, how much, how fast or how long.
• Examples of quantitative data in PE include time, distance, heart rate, score and number of repetitions.
• Quantitative data is usually objective because it is based on measurement rather than opinion.
• A questionnaire can collect quantitative data in PE when it asks closed questions with fixed answer options.
• A questionnaire asking students how many times they exercise each week produces quantitative data.
• A survey collects information from a group of people using the same questions so that responses can be counted and compared.
• Surveys are useful for quantitative data because they show numerical patterns and trends across a group.
• Quantitative data shows measurable outcomes of participation or performance in PE.
• Qualitative data in PE is descriptive information about qualities, experiences or opinions rather than numbers.
• Qualitative data explains what a performance was like and can help explain why a result occurred.
• Examples of qualitative data in PE include comments about technique, teamwork, confidence and decision making.
• Qualitative data is often subjective because different people may describe the same performance in different ways.
• An interview collects qualitative data in PE by asking a performer or observer to describe performance in detail.
• Interviews are useful for qualitative data because they allow extended answers rather than fixed numerical responses.
• An observation collects qualitative data in PE when a teacher, coach or peer records what they notice during performance.
• Observation data in PE can describe the quality of movement, the accuracy of technique and the effectiveness of tactics.
• Qualitative data gives detail about performance that numerical data alone cannot provide.
• Quantitative data measures performance numerically, whereas qualitative data describes the quality of performance.
• A results table presents data clearly by organising values into labelled rows and columns.
• The headings in a results table should state the variable being recorded and include units where needed.
• Units should be written in the heading of a table column rather than repeated in every cell.
• The independent variable is usually placed in the first column of a results table.
• The dependent variable is usually recorded in the next column so each result can be matched to the correct independent variable.
• Repeated trials can be recorded in separate columns before a calculated value such as a mean is added.
• A bar chart is used to compare different categories or discrete groups of data.
• The bars on a bar chart should be the same width and separated by gaps because each bar represents a separate category.
• In a bar chart, the categories or independent variable are usually shown on the x axis and the numerical values on the y axis.
• A line graph is used to show how data changes when the independent variable is continuous, such as time or distance.
• In a line graph, the independent variable is plotted on the x axis and the dependent variable is plotted on the y axis.
• The points on a line graph should be plotted accurately and then joined to show the overall trend in the data.
• The x axis and y axis on a bar chart or line graph must be labelled clearly so the reader knows what each axis represents.
• Axis labels on a bar chart or line graph should include units when the data is measured quantitatively.
• The scale on each axis should use regular intervals and cover the full range of the data being presented.
• Choosing an inappropriate scale can make differences or trends in the data appear larger or smaller than they really are.
• A bar chart makes it easier to compare values between categories at a glance.
• A line graph makes it easier to identify patterns, trends and rates of change in data collected over time.
• Accurate tables and graphs are important because incorrect labels, scales or plotting can lead to invalid conclusions about performance.
• Interpreting data in GCSE PE means reading and explaining information shown in tables, bar charts, line graphs and pie charts.
• A table allows exact values to be read, compared and ranked directly.
• To interpret a bar chart or line graph correctly, the variables and units shown on the axes must be identified before any conclusion is made.
• A bar chart is used to compare separate categories such as different performers, activities or fitness test scores.
• In a bar chart, the tallest or longest bar represents the greatest value, but in a timed fitness test the lowest value may indicate the best performance.
• A line graph is used to show how data changes over time or across another continuous variable.
• In a line graph, an upward trend shows an increase, a downward trend shows a decrease, and a flat line shows little or no change.
• A pie chart shows how a whole is divided into proportions, so all sectors together represent 100% and larger sectors represent larger percentages of the whole.
• Analysing PE data involves identifying patterns, trends, similarities, differences and anomalies in the results.
• An anomaly is a result that does not fit the general pattern of the data and may be caused by measurement error or unusual performance.
• Evaluating PE data involves making a justified judgement about what the results show about performance, health or training needs.
• Comparing results with previous performances, group data or normative values can identify strengths, weaknesses and realistic targets for improvement.

• Identify whether a PE result or comment is quantitative or qualitative data.
• Select questionnaires or surveys to collect quantitative data in a PE investigation.
• Select interviews or observations to collect qualitative data about performance.
• Record results in a table using clear headings, consistent units and correctly ordered variables.
• Place the independent variable in the first column and the dependent variable in the next column of a results table.
• Calculate a mean from repeated trials before presenting results.
• Plot a bar chart to compare separate performers, activities or categories.
• Plot a line graph to show changes across a continuous variable such as time or distance.
• Label the x axis and y axis correctly and include units where quantitative data is shown.
• Choose a scale that covers the full range of the data using regular intervals.
• Plot points accurately and join them correctly when drawing a line graph.
• Read exact values from a table, bar chart, line graph or pie chart.
• Interpret sectors in a pie chart as proportions of a whole.
• Compare categories, scores or trends shown in PE data.
• Interpret whether a higher or lower score represents better performance in a named context.
• Identify patterns, trends, similarities, differences and anomalies in a data set.
• Explain possible reasons for an anomalous result in PE data.
• Evaluate what the data shows about a performer's strengths, weaknesses or training needs.
• Justify conclusions by referring to previous results, group data or normative values.