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samples fluctuate by chance because participants differ and measurements vary. Inferential tests estimate whether the observed difference is larger than random variation would usually produce."},{"text":"Because data contain experimenter bias.","correct":false,"explanation":"Experimenter bias is a systematic influence from the researcher, not the ordinary chance fluctuation meant here. Two groups can look different with no real effect because of random variation in the data."},{"text":"Because data contain demand characteristics.","correct":false,"explanation":"Demand characteristics are cues that alter participants’ behaviour, not the statistical reason groups can differ by chance. The correct idea is random variation between samples or measurements."},{"text":"Because data contain population parameters.","correct":false,"explanation":"Population parameters are true values for the whole population, not a source of apparent sample differences. Apparent differences without a real effect are explained by random variation."}]},{"id":"1b73e03a-0111-43a6-9079-8158ad36b668","cardType":"cloze","points":null,"front":"Inferential testing uses [ ] to judge whether a result reflects a real effect in the [ ] rather than chance.","back":"Inferential testing uses sample data to judge whether a result reflects a real effect in the wider population rather than chance.","mcqOptions":[{"text":"Inferential testing uses sample data to judge whether a result reflects a real effect in the wider population rather than chance.","correct":true,"explanation":"Inferential testing starts with data from a sample and uses probability to decide whether the finding is likely to generalise to the wider population rather than being chance fluctuation."},{"text":"Inferential testing uses population data to judge whether a result reflects a real effect in the wider population rather than chance.","correct":false,"explanation":"This confuses samples with populations: researchers usually do not have data from the whole population. Inferential tests use sample data to make a judgement about the wider population."},{"text":"Inferential testing uses descriptive statistics to judge whether a result reflects a real effect in the wider population rather than chance.","correct":false,"explanation":"Descriptive statistics summarise what was found, but they do not by themselves test whether a finding is due to chance. Inferential testing uses sample data to infer about the wider population."},{"text":"Inferential testing uses sample data to judge whether a result reflects a real effect in the research sample rather than chance.","correct":false,"explanation":"This stops at the sample, which is what descriptive analysis mainly describes. Inferential testing uses sample data to judge whether the effect is likely to exist in the wider population."}]},{"id":"314cb2de-2af1-41a5-b725-6b535efb7411","cardType":"basic","points":1,"front":"Which hypothesis says any pattern in the data is due to chance?","back":"The null hypothesis.","mcqOptions":[{"text":"The null hypothesis.","correct":true,"explanation":"The null hypothesis states that any observed effect, difference, or relationship is due to chance rather than a genuine pattern in the population."},{"text":"The alternative hypothesis.","correct":false,"explanation":"The alternative hypothesis predicts a real effect, difference, or relationship. The hypothesis that says any pattern is due to chance is the null hypothesis."},{"text":"The directional hypothesis.","correct":false,"explanation":"A directional hypothesis predicts a real effect in a specific direction, such as one group scoring higher. The chance-only explanation is the null hypothesis."},{"text":"The research hypothesis.","correct":false,"explanation":"A research hypothesis usually proposes that there will be an effect or relationship. The hypothesis that attributes the pattern to chance is the null hypothesis."}]},{"id":"2fe76a65-c099-4aee-b304-60012ca564df","cardType":"basic","points":1,"front":"Which hypothesis states there is a real effect, difference, or relationship?","back":"The alternative hypothesis.","mcqOptions":[{"text":"The alternative hypothesis.","correct":true,"explanation":"The alternative hypothesis states that there is a genuine effect, difference, or relationship, rather than the observed result being explained purely by chance."},{"text":"The null hypothesis.","correct":false,"explanation":"The null hypothesis states that there is no real effect and that any observed pattern is due to chance. A real effect is stated by the alternative hypothesis."},{"text":"The descriptive hypothesis.","correct":false,"explanation":"Descriptive statistics summarise data, but “descriptive hypothesis” is not the standard hypothesis stating a real effect. The correct term is the alternative hypothesis."},{"text":"The sampling hypothesis.","correct":false,"explanation":"Sampling relates to how participants are selected, not to the formal prediction of a real effect. The hypothesis stating an effect, difference, or relationship is the alternative hypothesis."}]},{"id":"72c0f524-5cc4-408c-8017-dd7c563333fd","cardType":"cloze","points":null,"front":"A directional hypothesis usually leads to a [ ]; a non-directional hypothesis usually leads to a [ ].","back":"A directional hypothesis usually leads to a one-tailed test; a non-directional hypothesis usually leads to a two-tailed test.","mcqOptions":[{"text":"A directional hypothesis usually leads to a one-tailed test; a non-directional hypothesis usually leads to a two-tailed test.","correct":true,"explanation":"A directional hypothesis predicts the direction of the effect, so a one-tailed test is appropriate. A non-directional hypothesis allows effects in either direction, so it uses a two-tailed test."},{"text":"A directional hypothesis usually leads to a two-tailed test; a non-directional hypothesis usually leads to a one-tailed test.","correct":false,"explanation":"This reverses the rule. Directional predictions use one-tailed tests because only one direction is predicted, while non-directional predictions use two-tailed tests because either direction is possible."},{"text":"A directional hypothesis usually leads to a one-tailed test; a non-directional hypothesis usually leads to a one-tailed test.","correct":false,"explanation":"This wrongly treats both hypothesis types as directional. A non-directional hypothesis does not predict which way the effect will go, so it normally requires a two-tailed test."},{"text":"A directional hypothesis usually leads to a two-tailed test; a non-directional hypothesis usually leads to a two-tailed test.","correct":false,"explanation":"This wrongly treats a directional prediction as if both directions matter. A directional hypothesis usually leads to a one-tailed test; a non-directional hypothesis usually leads to a two-tailed test."}]},{"id":"3993b1b3-ca3c-4405-a3d9-d8fe54f0eb84","cardType":"basic","points":1,"front":"What does a p-value tell you if the null hypothesis is true?","back":"The probability of the observed or more extreme result occurring by chance.","mcqOptions":[{"text":"The probability of the observed or more extreme result occurring by chance.","correct":true,"explanation":"A p-value is calculated assuming the null hypothesis is true. It gives the probability of getting the observed result, or something even more extreme, by chance."},{"text":"The probability of the null hypothesis being true.","correct":false,"explanation":"This is the common Bayesian-style misreading of a p-value. A p-value assumes the null is true and gives the probability of the observed or more extreme result under that assumption."},{"text":"The probability of the alternative hypothesis being true.","correct":false,"explanation":"A p-value does not give the probability that the alternative hypothesis is true. It gives the probability of the observed or more extreme result occurring by chance if the null is true."},{"text":"The probability of the observed result being important.","correct":false,"explanation":"Statistical significance is not the same as practical importance. A p-value tells how likely the observed or more extreme result is by chance, assuming the null hypothesis is true."}]},{"id":"2a2829c1-3b3a-4398-abcd-deffa517338c","cardType":"basic","points":1,"front":"What does $p < 0.05$ mean?","back":"Less than a 5% probability the result occurred by chance if the null is true.","mcqOptions":[{"text":"Less than a 5% probability the result occurred by chance if the null is true.","correct":true,"explanation":"The statement means the probability of obtaining the observed result, or a more extreme one, is below 5% assuming the null hypothesis is true."},{"text":"Less than a 5% probability the null hypothesis is true.","correct":false,"explanation":"This confuses the probability of the data with the probability of the hypothesis. $p < 0.05$ means the result is unlikely by chance if the null is true, not that the null has under 5% probability."},{"text":"More than a 5% probability the result occurred by chance if the null is true.","correct":false,"explanation":"This reverses the inequality. $p < 0.05$ means the chance of the observed or more extreme result is less than 5%, not more than 5%, if the null is true."},{"text":"Less than a 0.5% probability the result occurred by chance if the null is true.","correct":false,"explanation":"This is a decimal-place slip: $0.05$ is 5%, while $0.005$ is 0.5%. The statement $p < 0.05$ sets the cut-off at less than 5%."}]},{"id":"0ce177a9-1038-4c16-ae2b-4e04a6ad6187","cardType":"cloze","points":null,"front":"$$p < 0.05$ does not mean there is a [ ] that the alternative hypothesis is [ ].","back":"$$p < 0.05$ does not mean there is a 95% chance that the alternative hypothesis is true.","mcqOptions":[{"text":"$$p < 0.05$ does not mean there is a 95% chance that the alternative hypothesis is true.","correct":true,"explanation":"A p-value is not the probability that a hypothesis is true. It is the probability of the observed or more extreme result if the null hypothesis is true."},{"text":"$$p < 0.05$ does not mean there is a 5% chance that the alternative hypothesis is true.","correct":false,"explanation":"The common misconception being tested is the 95% certainty claim, not a 5% chance for the alternative. P-values describe the probability of results under the null, not the probability of the alternative being true."},{"text":"$$p < 0.05$ does not mean there is a 95% chance that the alternative hypothesis is false.","correct":false,"explanation":"Changing “true” to “false” misses the specific misinterpretation: students often think significance proves the alternative with 95% certainty. The correct warning is that $p < 0.05$ does not mean a 95% chance the alternative is true."},{"text":"$$p < 0.05$ does not mean there is a 5% chance that the alternative hypothesis is false.","correct":false,"explanation":"This mixes up the p-value with the probability that a hypothesis is false. The intended point is that $p < 0.05$ does not mean a 95% chance that the alternative hypothesis is true."}]},{"id":"3861549e-e2bb-41c4-9e32-e6dd3a8d2bcd","cardType":"basic","points":1,"front":"What is a significance level used for?","back":"A cut-off for deciding whether a result is statistically significant.","mcqOptions":[{"text":"A cut-off for deciding whether a result is statistically significant.","correct":true,"explanation":"The significance level sets the probability threshold, such as $p < 0.05$, against which the result is judged to determine whether it is statistically significant."},{"text":"A measure of the strength of a relationship between two variables.","correct":false,"explanation":"This confuses the significance level with a correlation coefficient. A significance level is a cut-off used to decide whether a result is statistically significant."},{"text":"A score showing the size of the difference between two groups.","correct":false,"explanation":"This confuses statistical significance with effect size. The significance level provides a cut-off for judging significance, rather than measuring the size of a difference."},{"text":"A prediction about the direction of the experimental hypothesis.","correct":false,"explanation":"Direction is specified by the experimental hypothesis and determines whether a test is one-tailed or two-tailed. The significance level is the cut-off for statistical significance."}]},{"id":"85bf9a00-4831-4765-995c-c94b2007a2c8","cardType":"basic","points":1,"front":"If a result meets $p < 0.05$, what happens to the null hypothesis?","back":"The null hypothesis is rejected.","mcqOptions":[{"text":"The null hypothesis is rejected.","correct":true,"explanation":"A result meeting $p < 0.05$ has less than a 5% probability of occurring under the null hypothesis, so the null hypothesis is rejected."},{"text":"The null hypothesis is accepted.","correct":false,"explanation":"Accepting the null reverses the decision rule. When $p < 0.05$, the result is statistically significant and the null hypothesis is rejected."},{"text":"The null hypothesis is retained.","correct":false,"explanation":"The null hypothesis is retained when the result is not significant. Meeting $p < 0.05$ indicates significance, so the null hypothesis is rejected."},{"text":"The null hypothesis is proven.","correct":false,"explanation":"Hypothesis tests do not prove the null hypothesis, and a significant result counts against it. At $p < 0.05$, the null hypothesis is rejected."}]},{"id":"032e4f9c-f2b6-4b2e-8d8a-6c72874ea781","cardType":"basic","points":1,"front":"In a two-tailed test at $p < 0.05$, how much probability is in each tail?","back":"0.025 in each tail.","mcqOptions":[{"text":"0.025 in each tail.","correct":true,"explanation":"A two-tailed test divides the total significance level of $0.05$ equally between both tails, giving $0.025$ in each tail."},{"text":"0.05 in each tail.","correct":false,"explanation":"This fails to divide the significance level between the two tails and produces a total of $0.10$. Each tail must contain $0.025$."},{"text":"0.005 in each tail.","correct":false,"explanation":"This would be the allocation for a two-tailed test with an overall significance level of $0.01$. At $p < 0.05$, each tail contains $0.025$."},{"text":"0.01 in each tail.","correct":false,"explanation":"This incorrectly uses a familiar significance value without halving $0.05$. In a two-tailed test at $p < 0.05$, each tail contains $0.025$."}]},{"id":"6ae3bb8a-b373-4b52-bb94-155f2155b1cd","cardType":"cloze","points":null,"front":"In a [ ], all of the significance level is placed in [ ].","back":"In a one-tailed test, all of the significance level is placed in one tail.","mcqOptions":[{"text":"In a one-tailed test, all of the significance level is placed in one tail.","correct":true,"explanation":"A one-tailed test predicts a direction, so the entire significance level is placed in the single tail corresponding to that predicted direction."},{"text":"In a two-tailed test, all of the significance level is placed in one tail.","correct":false,"explanation":"A two-tailed test considers outcomes in both directions, so its significance level is divided between two tails. A one-tailed test places all of it in one tail."},{"text":"In a one-tailed test, all of the significance level is placed in two tails.","correct":false,"explanation":"This reverses the allocation rules. A one-tailed test places the entire significance level in one tail, whereas a two-tailed test divides it between two tails."},{"text":"In a two-tailed test, all of the significance level is placed in the central region.","correct":false,"explanation":"The central region represents results that do not reach significance. In a one-tailed test, the significance level is placed in one tail of the distribution."}]},{"id":"035fb6e6-7c74-4817-9f0f-a66effddae55","cardType":"basic","points":1,"front":"What is a critical value used for?","back":"It is the threshold the observed value must reach for significance.","mcqOptions":[{"text":"It is the threshold the observed value must reach for significance.","correct":true,"explanation":"The critical value marks the boundary for statistical significance. The observed value is compared with it using the decision rule appropriate to the statistical test."},{"text":"It is the probability that the null hypothesis is correct.","correct":false,"explanation":"A critical value is not the probability that the null hypothesis is true. It is the threshold the observed value must reach for statistical significance."},{"text":"It is the average of all the values found in the sample.","correct":false,"explanation":"This confuses a critical value with the sample mean. A critical value is a threshold used to judge whether the observed test statistic is significant."},{"text":"It is the score used to calculate the observed value.","correct":false,"explanation":"The observed value is calculated from the study’s data before comparison with the critical value. The critical value is the threshold required for significance."}]},{"id":"5391f8ba-6948-4dd3-bab0-89f4bad6f3dc","cardType":"cloze","points":null,"front":"For Spearman’s rho, Pearson’s r, Chi-square and t-tests, significance needs the observed value to be [ ] the critical value.","back":"For Spearman’s rho, Pearson’s r, Chi-square and t-tests, significance needs the observed value to be equal to or greater than the critical value.","mcqOptions":[{"text":"equal to or greater than","correct":true,"explanation":"For these tests, the result is significant when the observed value reaches or exceeds the critical value, so the comparison is observed value $\\geq$ critical value."},{"text":"equal to or less than","correct":false,"explanation":"This applies the decision rule used for tests such as Mann-Whitney U and Wilcoxon. Here, significance requires the observed value to equal or exceed the critical value."},{"text":"strictly less than","correct":false,"explanation":"This reverses the direction of the comparison and incorrectly excludes larger values. For these tests, the observed value must be equal to or greater than the critical value."},{"text":"exactly equal to","correct":false,"explanation":"Equality is sufficient but not necessary, because values above the threshold are also significant. The observed value must be equal to or greater than the critical value."}]},{"id":"28b22136-e862-42eb-ab25-0ee35f3ebce0","cardType":"cloze","points":null,"front":"For the Sign test, Mann-Whitney U and Wilcoxon, significance needs the observed value to be [ ] the critical value.","back":"For the Sign test, Mann-Whitney U and Wilcoxon, significance needs the observed value to be equal to or less than the critical value.","mcqOptions":[{"text":"equal to or less than","correct":true,"explanation":"For these tests, lower observed values provide stronger evidence against the null hypothesis. Significance therefore requires the observed value to equal or fall below the critical value."},{"text":"equal to or greater than","correct":false,"explanation":"This uses the decision rule for tests such as Spearman’s rho and Chi-square. For these tests, the observed value must be equal to or less than the critical value."},{"text":"strictly greater than","correct":false,"explanation":"This reverses the required direction and wrongly excludes the critical value itself. Significance occurs when the observed value is equal to or less than the critical value."},{"text":"exactly equal to","correct":false,"explanation":"An observed value does not need to match the critical value exactly. Any value below it is also significant, so the rule is equal to or less than."}]},{"id":"317a2601-2c0f-469a-ac6c-a837578bcd7b","cardType":"basic","points":2,"front":"Wilcoxon: observed value $8$, critical value $10$. Is the result significant?","back":"Yes - for Wilcoxon, $8 \\leq 10$.","mcqOptions":[{"text":"Yes - for Wilcoxon, $8 \\leq 10$.","correct":true,"explanation":"Wilcoxon is significant when the observed value is equal to or less than the critical value. Since $8$ is below $10$, the result is significant."},{"text":"No - for Wilcoxon, $8 \\leq 10$.","correct":false,"explanation":"This identifies the comparison correctly but draws the opposite conclusion. For Wilcoxon, an observed value equal to or below the critical value means the result is significant."},{"text":"No - for Wilcoxon, the observed value must be greater than $10$.","correct":false,"explanation":"This reverses Wilcoxon’s decision rule by treating larger values as significant. The observed value must be equal to or less than the critical value, and $8 \\leq 10$."},{"text":"Yes - for Wilcoxon, $8 \\geq 10$.","correct":false,"explanation":"The conclusion is right but the comparison is mathematically false and uses the wrong direction. The result is significant because Wilcoxon requires $8 \\leq 10$."}]},{"id":"e0a7739e-d5e5-46dc-ba73-777e31d7308b","cardType":"basic","points":2,"front":"What can a significant result let the researcher conclude?","back":"Reject the null; the alternative is supported, not proven.","mcqOptions":[{"text":"Reject the null; the alternative is supported, not proven.","correct":true,"explanation":"A significant result provides enough evidence to reject the null hypothesis and support the alternative, but statistical testing does not prove the alternative with certainty."},{"text":"Reject the null; the alternative is proven.","correct":false,"explanation":"Significance supports the alternative but cannot prove it, because sampling variation and the chosen significance level still leave a possibility of error. The correct conclusion is supported, not proven."},{"text":"Retain the null; the alternative is unsupported.","correct":false,"explanation":"This reverses the decision rule: a significant result leads to rejecting the null, not retaining it. The alternative is supported, although it is not proven."},{"text":"Reject the alternative; the null is supported, not proven.","correct":false,"explanation":"A significant result is evidence against the null rather than against the alternative. Researchers reject the null and support the alternative, without claiming it is proven."}]},{"id":"d04253aa-90cf-4865-bd54-6ff832611d42","cardType":"basic","points":2,"front":"What can a non-significant result let the researcher conclude?","back":"Retain the null; there is insufficient evidence against it.","mcqOptions":[{"text":"Retain the null; there is insufficient evidence against it.","correct":true,"explanation":"A non-significant result does not provide enough evidence to reject the null hypothesis, so the researcher retains it while recognising that this is not proof that it is true."},{"text":"Reject the null; there is sufficient evidence against it.","correct":false,"explanation":"This is the conclusion for a significant result, not a non-significant one. With insufficient evidence against the null, the researcher should retain the null."},{"text":"Prove the null; there is no evidence against it.","correct":false,"explanation":"A non-significant result cannot prove the null or show that no evidence exists against it. It only means there is insufficient evidence to reject the null."},{"text":"Retain the alternative; there is insufficient evidence for the null.","correct":false,"explanation":"The decision concerns retaining the null when the result is non-significant. Insufficient evidence against the null does not establish the alternative instead."}]},{"id":"040e9f08-b905-49ed-9b5b-e50a04977e23","cardType":"basic","points":1,"front":"A researcher rejects the null when no real effect exists. What error is this?","back":"A Type I error.","mcqOptions":[{"text":"A Type I error.","correct":true,"explanation":"A Type I error occurs when the null hypothesis is rejected even though it is actually true, meaning the researcher claims an effect that does not exist."},{"text":"A Type II error.","correct":false,"explanation":"A Type II error is the opposite mistake: retaining the null when a real effect exists. Rejecting a true null is a Type I error."},{"text":"A sampling error.","correct":false,"explanation":"Sampling error describes differences caused by studying a sample rather than the population, but the specific decision error here is rejecting a true null: a Type I error."},{"text":"A researcher effect.","correct":false,"explanation":"A researcher effect is influence from the investigator on participants or procedures, not an incorrect hypothesis decision. Rejecting the null when no effect exists is a Type I error."}]},{"id":"5284045f-a9f5-4de7-a1b6-a75b25068c3a","cardType":"basic","points":1,"front":"A researcher retains the null when a real effect exists. What error is this?","back":"A Type II error.","mcqOptions":[{"text":"A Type II error.","correct":true,"explanation":"A Type II error occurs when the null hypothesis is retained even though a real effect exists, so the study fails to detect a genuine difference or relationship."},{"text":"A Type I error.","correct":false,"explanation":"A Type I error involves rejecting a true null and claiming a nonexistent effect. Retaining the null when an effect is real is a Type II error."},{"text":"A measurement error.","correct":false,"explanation":"Measurement error comes from inaccurate or inconsistent measurement, whereas this is a hypothesis-testing decision error. Missing a real effect by retaining the null is Type II."},{"text":"A participant variable.","correct":false,"explanation":"A participant variable is an individual characteristic that may influence results, not an incorrect significance decision. Retaining the null despite a real effect is a Type II error."}]},{"id":"4a460283-9582-43e6-9707-985e705731eb","cardType":"basic","points":3,"front":"How does changing the significance level affect Type I and Type II errors?","back":"

Stricter levels reduce Type I risk.
Stricter levels increase Type II risk.

","mcqOptions":[{"text":"

Stricter levels reduce Type I risk.
Stricter levels increase Type II risk.

","correct":true,"explanation":"A stricter significance level makes rejecting the null harder, reducing false positives or Type I errors. However, it also makes genuine effects easier to miss, increasing Type II errors."},{"text":"

Stricter levels increase Type I risk.
Stricter levels reduce Type II risk.

","correct":false,"explanation":"This reverses both effects. Making significance stricter reduces the chance of wrongly rejecting a true null, but increases the chance of retaining it when a real effect exists."},{"text":"

Stricter levels reduce Type I risk.
Stricter levels also reduce Type II risk.

","correct":false,"explanation":"Although stricter levels reduce Type I risk, they do not reduce both errors. The harder rejection threshold increases the risk of missing a genuine effect, producing more Type II errors."},{"text":"

Stricter levels increase Type I risk.
Stricter levels also increase Type II risk.

","correct":false,"explanation":"A stricter threshold increases Type II risk but reduces Type I risk. Rejecting the null becomes less likely, so false-positive conclusions become less common."}]},{"id":"2e45280a-52a4-47bb-9c3b-07524dde369f","cardType":"basic","points":3,"front":"How can sample size distort significance decisions?","back":"

Large samples can make tiny effects significant.
Small samples can miss meaningful effects.

","mcqOptions":[{"text":"

Large samples can make tiny effects significant.
Small samples can miss meaningful effects.

","correct":true,"explanation":"Large samples provide greater statistical power, so even small effects may reach significance. Small samples have lower power and can fail to detect meaningful effects, increasing Type II risk."},{"text":"

Large samples can hide tiny effects.
Small samples can make meaningful effects significant.

","correct":false,"explanation":"This reverses the effect of sample size. Large samples are more sensitive to small effects, whereas small samples have lower power and may miss genuine effects."},{"text":"

Large samples can make tiny effects significant.
Small samples can prove meaningful effects.

","correct":false,"explanation":"Large samples can make tiny effects statistically significant, but small samples cannot prove meaningful effects. Their limited power may instead cause genuine effects to be missed."},{"text":"

Large samples can make all effects significant.
Small samples can never detect meaningful effects.

","correct":false,"explanation":"Sample size affects power but does not guarantee significance or make detection impossible. Large samples can reveal tiny effects, while small samples can still detect sufficiently strong effects."}]},{"id":"2b715e0a-51b0-4e8f-bd96-cba44b99ae64","cardType":"basic","points":2,"front":"Why does significance testing improve scientific credibility?","back":"It gives an objective decision rule others can check.","mcqOptions":[{"text":"It gives an objective decision rule others can check.","correct":true,"explanation":"A predefined significance rule applies the same statistical criterion to results, making the decision transparent, reproducible and open for other researchers to evaluate."},{"text":"It gives a subjective decision rule others can interpret freely.","correct":false,"explanation":"Subjective interpretation would reduce consistency and make findings harder to compare. Significance testing improves credibility by providing an objective decision rule that others can check."},{"text":"It gives an objective proof others must accept.","correct":false,"explanation":"Statistical significance does not prove a hypothesis or force acceptance. It improves credibility by offering an objective, checkable decision rule while acknowledging uncertainty."},{"text":"It gives a private decision rule only the researcher can check.","correct":false,"explanation":"The value of significance testing is its transparency, not privacy. An objective decision rule allows other researchers to inspect and evaluate how the conclusion was reached."}]},{"id":"be922df6-3505-49d0-ac8e-7c870a68f472","cardType":"cloze","points":null,"front":"The $p < 0.05$ cut-off is a [ ], not a [ ] around 0.05.","back":"The $p < 0.05$ cut-off is a convention, not a magical difference around 0.05.","mcqOptions":[{"text":"The $p < 0.05$ cut-off is a convention, not a magical difference around 0.05.","correct":true,"explanation":"The 0.05 threshold is a widely used convention for making decisions, not a natural point where results suddenly become meaningfully different on either side."},{"text":"The $p < 0.05$ cut-off is a law, not a magical difference around 0.05.","correct":false,"explanation":"The threshold is not an absolute scientific law; researchers conventionally use it as a decision rule. It also does not create a magical difference between nearby p-values."},{"text":"The $p < 0.05$ cut-off is a convention, not a meaningful difference around 0.05.","correct":false,"explanation":"The threshold is a convention, but results just below and above 0.05 can differ in statistical decision without representing a meaningful difference. The correct term is magical difference."},{"text":"The $p < 0.05$ cut-off is a proof, not a magical difference around 0.05.","correct":false,"explanation":"A p-value below 0.05 does not prove the alternative hypothesis; it meets a conventional significance criterion. The cut-off also is not a magical difference around 0.05."}]}]}],"examCountdown":{"examDate":"2027-05-18","paperLabel":"Paper 1"},"heading":"Probability and significance","title":"Probability and significance Flashcards","topbar":{"backHref":"/a-level/psychology/aqa/flashcards"},"topicSidebar":"$L39"}]}],"topicId":"91ef2672-d434-5de7-a1a0-768fbf5fbaac","walled":"$L3a"}],"$L3b","$L3c"]