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A memory score, reaction time or number of errors is numerical, so it is quantitative data."},{"text":"Nominal data.","correct":false,"explanation":"Nominal data consist of named, unordered categories rather than numerical measurements. These examples are scores, measurements or counts, so they are quantitative data."},{"text":"Secondary data.","correct":false,"explanation":"Secondary data are obtained from sources originally produced by someone else. The numerical nature of these results means they are quantitative data."}]},{"id":"bc61623d-8323-4b0a-99ff-bf31c5a0f315","cardType":"basic","points":1,"front":"What is one participant's individual result called?","back":"A raw score.","mcqOptions":[{"text":"A raw score.","correct":true,"explanation":"A raw score is the original individual result obtained by one participant before researchers calculate summary statistics or otherwise process the data."},{"text":"A mean score.","correct":false,"explanation":"A mean score is an average calculated from multiple scores, not one participant's individual result. An individual unprocessed result is a raw score."},{"text":"A standard score.","correct":false,"explanation":"A standard score expresses a result relative to a distribution after conversion. The participant's original individual result is called a raw score."},{"text":"A total score.","correct":false,"explanation":"A total score combines multiple values by addition and need not represent one unprocessed result. One participant's original individual result is a raw score."}]},{"id":"25aab0ff-8f84-47ca-bbed-c604102caecb","cardType":"basic","points":1,"front":"What is the number of times a score or category occurs called?","back":"A frequency.","mcqOptions":[{"text":"A frequency.","correct":true,"explanation":"Frequency is the count of how many times a particular score, value or category appears within a set of data."},{"text":"A mode.","correct":false,"explanation":"The mode is the score or category occurring most often, rather than the number of occurrences itself. That number is its frequency."},{"text":"A percentage.","correct":false,"explanation":"A percentage expresses a frequency as a proportion out of 100. The actual number of times a score or category occurs is its frequency."},{"text":"A raw score.","correct":false,"explanation":"A raw score is one participant's original result, not a count of repeated occurrences. The number of occurrences is called a frequency."}]},{"id":"d92ef55d-12ce-499f-b5e2-33324178fbca","cardType":"basic","points":1,"front":"In correlational research, what are the two measured variables called?","back":"Co-variables.","mcqOptions":[{"text":"Co-variables.","correct":true,"explanation":"Correlational research measures two co-variables and assesses how they vary together; it does not manipulate an independent variable or measure its causal effect."},{"text":"Independent variables.","correct":false,"explanation":"Independent variables are manipulated in experiments. Correlational research measures two variables without manipulation, so both are described as co-variables."},{"text":"Dependent variables.","correct":false,"explanation":"Dependent variables are measured as outcomes in experiments. Because neither measured variable has this designated causal role in a correlation, they are co-variables."},{"text":"Extraneous variables.","correct":false,"explanation":"Extraneous variables are unwanted factors that may affect findings, not the two variables deliberately measured for association. Those measured variables are co-variables."}]},{"id":"4c2a4ed9-8a3f-4e63-8b64-de636a6c997c","cardType":"cloze","points":null,"front":"Choosing 'help' or 'do not help' produces [ ]; ranking anxiety from low to high produces [ ].","back":"Choosing 'help' or 'do not help' produces nominal data; ranking anxiety from low to high produces ordinal data.","mcqOptions":[{"text":"Choosing 'help' or 'do not help' produces nominal data; ranking anxiety from low to high produces ordinal data.","correct":true,"explanation":"The help decision places responses into unordered named categories, making it nominal data. Anxiety rankings have a meaningful order but unequal or unknown intervals, making them ordinal data."},{"text":"Choosing 'help' or 'do not help' produces ordinal data; ranking anxiety from low to high produces nominal data.","correct":false,"explanation":"This reverses the levels of measurement. Help choices are unordered categories and therefore nominal, whereas ranked anxiety positions are ordered and therefore ordinal."},{"text":"Choosing 'help' or 'do not help' produces interval data; ranking anxiety from low to high produces ordinal data.","correct":false,"explanation":"A binary choice does not have equal numerical intervals, so it is nominal rather than interval data. The ranked anxiety data are correctly identified as ordinal."},{"text":"Choosing 'help' or 'do not help' produces nominal data; ranking anxiety from low to high produces ratio data.","correct":false,"explanation":"Ranking establishes order but does not provide a true zero or meaningful ratios, so anxiety rankings are ordinal rather than ratio data. The help choice is nominal."}]},{"id":"3f8fd1b7-4dee-46b4-8cc6-dced8fa4721e","cardType":"cloze","points":null,"front":"[ ] can take many values along a scale; [ ] come in separate values.","back":"Continuous data can take many values along a scale; discrete data come in separate values.","mcqOptions":[{"text":"Continuous data can take many values along a scale; discrete data come in separate values.","correct":true,"explanation":"Continuous data can take any value within a range, subject to measurement precision. Discrete data are restricted to distinct, separate values, commonly whole-number counts."},{"text":"Discrete data can take many values along a scale; continuous data come in separate values.","correct":false,"explanation":"This reverses the distinction. Continuous data can take many possible values along a scale, while discrete data consist of distinct, separate values."},{"text":"Continuous data can take many values along a scale; continuous data come in separate values.","correct":false,"explanation":"The first classification is correct, but continuous data do not come only in separate values. The second blank should identify discrete data."},{"text":"Categorical data can take many values along a scale; continuous data come in separate values.","correct":false,"explanation":"Categorical data represent groups rather than positions along a numerical scale, while continuous data are not confined to separate values. The correct pair is continuous and discrete."}]},{"id":"f15f6c85-a85a-46ba-ba4f-55dd829bad08","cardType":"basic","points":1,"front":"Which display is best when exact numerical values must be read clearly?","back":"A table.","mcqOptions":[{"text":"A table.","correct":true,"explanation":"A table presents numerical results in labelled rows and columns, allowing readers to locate and compare exact values more clearly than in a visual graph."},{"text":"A histogram.","correct":false,"explanation":"A histogram clearly displays the shape and frequency distribution of continuous data, but exact individual numerical values are generally easier to read from a table."},{"text":"A bar chart.","correct":false,"explanation":"A bar chart makes category comparisons visually accessible, but bar heights do not communicate exact values as clearly and precisely as entries in a table."},{"text":"A scattergram.","correct":false,"explanation":"A scattergram is designed to reveal the direction and strength of a relationship between co-variables, not to present exact numerical values clearly. A table is preferable."}]},{"id":"f6b48451-3ef8-4245-82ad-516d0a829d9b","cardType":"basic","points":2,"front":"Why include the range as well as the mean in a summary table?","back":"It shows spread, not just the average.","mcqOptions":[{"text":"It shows spread, not just the average.","correct":true,"explanation":"The mean describes central tendency but not variation. Including the range shows how spread out the scores are, providing a fuller summary of the distribution."},{"text":"It shows central tendency, not just the average.","correct":false,"explanation":"The mean already describes central tendency, so this would add no information about variation. The range is included because it shows the spread of scores."},{"text":"It shows frequency, not just the average.","correct":false,"explanation":"Frequency is the number of times a value occurs and cannot be determined from the range. The range supplements the mean by showing the data's spread."},{"text":"It shows correlation, not just the average.","correct":false,"explanation":"Correlation describes the relationship between two co-variables, whereas the range summarises variation within one set of scores. It is included to show spread."}]},{"id":"b632081d-7c41-4f99-92a0-ec4927515b41","cardType":"basic","points":1,"front":"How should raw participant scores be labelled to protect confidentiality?","back":"Use anonymised participant labels, not names.","mcqOptions":[{"text":"Use anonymised participant labels, not names.","correct":true,"explanation":"Anonymised labels, such as participant numbers or codes, allow individual scores to be distinguished without revealing identities, thereby protecting participants’ confidentiality."},{"text":"Use participants’ full names, not labels.","correct":false,"explanation":"Full names make scores directly identifiable and breach confidentiality. Researchers should use anonymised participant labels so results cannot readily be linked to particular individuals."},{"text":"Use participants’ initials, not codes.","correct":false,"explanation":"Initials may still allow participants to be identified, especially within a small sample. Anonymised participant labels provide stronger protection by avoiding personal identifiers."},{"text":"Use descriptive group labels, not codes.","correct":false,"explanation":"Group labels do not distinguish each participant’s raw score and may reveal personal characteristics. Individual scores should instead use anonymised participant labels."}]},{"id":"31ed317a-ccfb-46fa-8a13-55ae004dbe6a","cardType":"cloze","points":null,"front":"In experiments, the [ ] usually goes on the x-axis and the [ ] on the y-axis.","back":"In experiments, the independent variable usually goes on the x-axis and the dependent variable on the y-axis.","mcqOptions":[{"text":"In experiments, the independent variable usually goes on the x-axis and the dependent variable on the y-axis.","correct":true,"explanation":"The independent variable represents the conditions manipulated or compared, so it appears on the x-axis. The measured dependent variable is then plotted on the y-axis."},{"text":"In experiments, the dependent variable usually goes on the x-axis and the independent variable on the y-axis.","correct":false,"explanation":"This reverses the standard axis convention. The independent variable usually belongs on the x-axis, while the dependent variable measured in each condition belongs on the y-axis."},{"text":"In experiments, the control variable usually goes on the x-axis and the dependent variable on the y-axis.","correct":false,"explanation":"Control variables are held constant rather than plotted as experimental conditions. The independent variable normally goes on the x-axis and the dependent variable on the y-axis."},{"text":"In experiments, the independent variable usually goes on the x-axis and the extraneous variable on the y-axis.","correct":false,"explanation":"Extraneous variables are not the intended outcome measure and should normally be controlled. The dependent variable, which records the measured effect, belongs on the y-axis."}]},{"id":"8757f235-eaf3-4bf9-8e47-751629fb3724","cardType":"basic","points":1,"front":"Which graph is used to compare separate categories, groups or conditions?","back":"A bar chart.","mcqOptions":[{"text":"A bar chart.","correct":true,"explanation":"A bar chart displays results for distinct categories, groups or experimental conditions. Separate bars make differences between their frequencies, means or percentages easy to compare."},{"text":"A histogram.","correct":false,"explanation":"A histogram represents continuous data grouped into adjacent intervals, not separate categories. A bar chart is used when comparing distinct groups, categories or conditions."},{"text":"A line graph.","correct":false,"explanation":"A line graph is usually used to show change across an ordered or continuous variable, such as time. Separate categories or conditions are compared using a bar chart."},{"text":"A scattergram.","correct":false,"explanation":"A scattergram displays the relationship or correlation between two co-variables. A bar chart is appropriate for comparing separate categories, groups or experimental conditions."}]},{"id":"a861dc4d-c724-4955-bfb0-4f287371165b","cardType":"cloze","points":null,"front":"In a bar chart, bars should [ ] because the categories are [ ].","back":"In a bar chart, bars should not touch because the categories are separate.","mcqOptions":[{"text":"In a bar chart, bars should not touch because the categories are separate.","correct":true,"explanation":"Bar charts represent discrete categories, so gaps between the bars visually show that each group or condition is distinct from the others."},{"text":"In a bar chart, bars should touch because the categories are continuous.","correct":false,"explanation":"This describes a histogram rather than a bar chart. Bar-chart categories are separate, so their bars should not touch and must have visible gaps."},{"text":"In a bar chart, bars should not touch because the categories are continuous.","correct":false,"explanation":"The spacing is correct, but the reason is not: bar-chart categories are separate, not continuous. Continuous intervals are represented by touching bars in a histogram."},{"text":"In a bar chart, bars should touch because the categories are separate.","correct":false,"explanation":"Separate categories require visible gaps, so bar-chart bars should not touch. Touching bars wrongly suggest continuous intervals, as found in a histogram."}]},{"id":"14c244cd-5c09-40cd-a988-caa0ccf50eaa","cardType":"basic","points":1,"front":"Which graph is best for showing the distribution of continuous grouped scores?","back":"A histogram.","mcqOptions":[{"text":"A histogram.","correct":true,"explanation":"A histogram shows the distribution of continuous scores grouped into intervals. Its adjacent bars represent intervals that follow continuously with no genuine gaps between them."},{"text":"A bar chart.","correct":false,"explanation":"A bar chart compares separate categories, groups or conditions rather than continuous score intervals. A histogram is used to display a distribution of grouped continuous scores."},{"text":"A line graph.","correct":false,"explanation":"A line graph commonly shows change across an ordered variable, such as time. A histogram is the appropriate graph for a frequency distribution of grouped continuous scores."},{"text":"A scattergram.","correct":false,"explanation":"A scattergram plots paired scores to investigate a correlation between two co-variables. A histogram instead displays the distribution of continuous scores grouped into intervals."}]},{"id":"3baaf2ce-c706-4ecf-bb5e-67fac315192c","cardType":"basic","points":2,"front":"Why do histogram bars touch?","back":"The intervals are continuous, with no real gaps.","mcqOptions":[{"text":"The intervals are continuous, with no real gaps.","correct":true,"explanation":"Each interval continues directly into the next because the measured variable is continuous. Touching bars accurately show that there are no genuine breaks between possible scores."},{"text":"The intervals are separate, with deliberate gaps.","correct":false,"explanation":"Separate categories with gaps describe a bar chart, not a histogram. Histogram bars touch because their class intervals form a continuous scale with no genuine gaps."},{"text":"The bars show averages, rather than frequencies.","correct":false,"explanation":"Whether values are averages does not determine whether bars touch. Histogram bars touch because they represent adjacent intervals on a continuous scale."},{"text":"The intervals are equal-width, with matching frequencies.","correct":false,"explanation":"Equal interval widths improve fair visual comparison, but they do not explain why bars touch. The bars touch because the score intervals are continuous."}]},{"id":"09a7ca94-b2a1-4f31-af18-65bde928fab0","cardType":"basic","points":1,"front":"In a histogram of score intervals, what should the y-axis show?","back":"Frequency.","mcqOptions":[{"text":"Frequency.","correct":true,"explanation":"The y-axis shows how many scores fall within each interval, which is the frequency. The score intervals themselves are displayed along the x-axis."},{"text":"Score interval.","correct":false,"explanation":"Score intervals belong on the x-axis because they define the grouped ranges of the continuous variable. The y-axis shows the frequency within each interval."},{"text":"Mean score.","correct":false,"explanation":"A histogram displays an entire score distribution rather than plotting one mean for each interval. Its y-axis shows the frequency of scores in each interval."},{"text":"Category label.","correct":false,"explanation":"Category labels are associated with separate bars in a bar chart. A histogram uses continuous score intervals on the x-axis and frequency on the y-axis."}]},{"id":"77bbc8b7-5044-4638-8d0d-50247d40187f","cardType":"basic","points":2,"front":"Why should A-Level histograms usually use equal-width intervals?","back":"Unequal intervals can mislead about frequency.","mcqOptions":[{"text":"Unequal intervals can mislead about frequency.","correct":true,"explanation":"When ordinary bar heights are used, wider intervals can appear more prominent simply because of their width. Equal-width intervals make visual comparisons of frequencies fairer."},{"text":"Unequal intervals can increase the total frequency.","correct":false,"explanation":"Changing interval widths does not create additional observations or alter the total frequency. Equal widths are preferred because unequal widths can visually mislead comparisons."},{"text":"Equal intervals can remove extreme scores.","correct":false,"explanation":"Grouping scores into equal intervals does not remove outliers or alter the original data. Equal-width intervals are used to avoid misleading visual comparisons of frequency."},{"text":"Equal intervals can prove that data are normal.","correct":false,"explanation":"Equal interval widths cannot establish that a distribution is normal; its overall shape must be examined. Their purpose is to prevent misleading comparisons of frequency."}]},{"id":"191e6fa2-4d8b-4f93-afc1-a829abbbd8a2","cardType":"basic","points":1,"front":"Which graph plots paired scores for two co-variables?","back":"A scattergram.","mcqOptions":[{"text":"A scattergram.","correct":true,"explanation":"A scattergram plots paired scores for two co-variables, with each point representing one participant’s scores on both variables. The pattern of points can then show the direction and strength of correlation."},{"text":"A bar chart.","correct":false,"explanation":"A bar chart compares separate categories rather than plotting paired scores for two co-variables. The correct graph is a scattergram, where each point represents one pair of scores."},{"text":"A histogram.","correct":false,"explanation":"A histogram displays the frequency distribution of continuous data and does not pair scores from two variables. A scattergram is used to display the relationship between co-variables."},{"text":"A frequency polygon.","correct":false,"explanation":"A frequency polygon shows the shape of a single frequency distribution by joining plotted frequencies. Paired scores for two co-variables are plotted using a scattergram."}]},{"id":"4cdd168b-68c4-40aa-8b72-cecf2b107bb3","cardType":"basic","points":1,"front":"If scattergram points slope downwards left to right, what correlation is suggested?","back":"A negative correlation.","mcqOptions":[{"text":"A negative correlation.","correct":true,"explanation":"A downward slope from left to right means that higher scores on one variable tend to be associated with lower scores on the other, indicating a negative correlation."},{"text":"A positive correlation.","correct":false,"explanation":"A positive correlation slopes upwards as one variable increases alongside the other. A downward slope indicates a negative correlation because the variables move in opposite directions."},{"text":"No correlation","correct":false,"explanation":"No correlation would produce points scattered without a clear upward or downward trend. A consistent downward slope suggests a negative correlation between the two variables."},{"text":"A zero-order correlation.","correct":false,"explanation":"Zero-order describes a correlation calculated without controlling for another variable, not the direction shown by the slope. A downward trend specifically indicates a negative correlation."}]},{"id":"db429c55-09e0-4a0b-87eb-873cf603cf13","cardType":"basic","points":2,"front":"Why should a researcher avoid claiming causation from a scattergram?","back":"It shows association; a third variable may explain it.","mcqOptions":[{"text":"It shows association; a third variable may explain it.","correct":true,"explanation":"A scattergram can show that two variables are associated, but it cannot establish that one causes the other. An unmeasured third variable may influence both variables."},{"text":"It proves causation; a third variable cannot explain it.","correct":false,"explanation":"A correlation does not prove causation because the relationship may result from a third variable or reverse causation. A scattergram only demonstrates association between variables."},{"text":"It shows association; the two variables must directly cause each other.","correct":false,"explanation":"Association alone does not mean that the variables directly cause each other. A third variable, or causation operating in the opposite direction, could explain the observed relationship."},{"text":"It shows reliability; measurement error may explain it.","correct":false,"explanation":"Reliability concerns the consistency of measurement, whereas a scattergram displays association between variables. The key reason causation cannot be claimed is that a third variable may explain the association."}]},{"id":"9a7a2429-2755-43bb-a0b6-00fce3a2ddc2","cardType":"cloze","points":null,"front":"A table or graph can suggest a pattern, but statistical significance needs an [ ].","back":"A table or graph can suggest a pattern, but statistical significance needs an inferential statistical test.","mcqOptions":[{"text":"inferential statistical test","correct":true,"explanation":"Inferential statistical tests assess whether an observed pattern is likely to reflect a genuine effect rather than chance. Tables and graphs describe patterns but cannot establish statistical significance alone."},{"text":"descriptive statistical test","correct":false,"explanation":"Descriptive statistics summarise or display the data but do not test whether a finding is statistically significant. Statistical significance requires an inferential statistical test."},{"text":"correlational statistical test","correct":false,"explanation":"A correlational test is appropriate only for testing relationships between variables and is not the general requirement for statistical significance. The required term is inferential statistical test."},{"text":"qualitative statistical test","correct":false,"explanation":"Qualitative data are generally analysed using non-numerical methods, so this is not the relevant term for testing significance in quantitative results. The correct term is inferential statistical test."}]},{"id":"d04e8f77-1f8a-4b07-a5fc-1e3f71b32a3a","cardType":"cloze","points":null,"front":"A [ ] can exaggerate differences; too many decimal places can imply [ ].","back":"A truncated y-axis can exaggerate differences; too many decimal places can imply false precision.","mcqOptions":[{"text":"A truncated y-axis can exaggerate differences; too many decimal places can imply false precision.","correct":true,"explanation":"Starting the y-axis above zero can make differences look larger than they are, while excessive decimal places suggest a level of measurement accuracy the data may not support."},{"text":"A truncated x-axis can exaggerate differences; too many decimal places can imply false precision.","correct":false,"explanation":"It is a truncated y-axis that commonly exaggerates vertical differences in a graph. Too many decimal places can imply false precision, so only the first filled term is wrong."},{"text":"A truncated y-axis can exaggerate differences; too many decimal places can imply sampling error.","correct":false,"explanation":"Excessive decimal places suggest false precision, not sampling error. Sampling error concerns how a sample differs from its population, whereas graph scale and reporting detail affect presentation."},{"text":"A bar chart can exaggerate differences; too many decimal places can imply false precision.","correct":false,"explanation":"A bar chart does not inherently exaggerate differences; the misleading feature is a truncated y-axis that begins above zero. The second term correctly identifies false precision."}]},{"id":"c5c884fc-ddd2-462d-802a-5921c1e39b4d","cardType":"cloze","points":null,"front":"For correlations, ordinal or ranked data suit [ ]; interval/ratio linear data suit [ ].","back":"For correlations, ordinal or ranked data suit Spearman's rho; interval/ratio linear data suit Pearson's r.","mcqOptions":[{"text":"For correlations, ordinal or ranked data suit Spearman's rho; interval/ratio linear data suit Pearson's r.","correct":true,"explanation":"Spearman's rho is suitable for ordinal or ranked data, while Pearson's r is suitable for interval or ratio data when the relationship is linear and appropriate assumptions are met."},{"text":"For correlations, ordinal or ranked data suit Pearson's r; interval/ratio linear data suit Spearman's rho.","correct":false,"explanation":"The tests have been reversed: Spearman's rho is used for ordinal or ranked data, whereas Pearson's r is used for interval or ratio data with a linear relationship."},{"text":"For correlations, ordinal or ranked data suit chi-squared test; interval/ratio linear data suit Pearson's r.","correct":false,"explanation":"A chi-squared test examines associations between categorical variables and is not the appropriate correlation measure for ordinal or ranked data. Spearman's rho should be used instead."},{"text":"For correlations, ordinal or ranked data suit Spearman's rho; interval/ratio linear data suit chi-squared test.","correct":false,"explanation":"A chi-squared test is designed for categorical frequency data, not for measuring a linear correlation between interval or ratio scores. Pearson's r is the appropriate second test."}]}]}],"examCountdown":{"examDate":"2027-05-18","paperLabel":"Paper 1"},"heading":"Presentation of quantitative data (A-level only)","title":"Presentation of quantitative data (A-level only) Flashcards","topbar":{"backHref":"/a-level/psychology/aqa/flashcards"},"topicSidebar":"$L38"}]}],"topicId":"c58ae4c7-2758-548d-8747-10106daea20c","walled":"$L39"}],"$L3a","$L3b"]