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関連する概念動画

Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Testing a Claim about Standard Deviation01:19

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
282
Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
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すみません 邪魔しましたか? テスト前の情報とテスト後の情報のパフォーマンスと侵入率の比較

Kelsey K James1, Benjamin C Storm2

  • 1Department of Psychology, University of Houston Clear Lake, Houston, TX 77058, USA.

Behavioral sciences (Basel, Switzerland)
|August 28, 2025
PubMed
まとめ
この要約は機械生成です。

プレテストとポストテストの両方が学習を促進しますが,プレテストはポストテストよりも誤った情報の侵入を効果的に軽減します. フィードバックがあったとしても テスト後の記憶は 誤った情報を増やしました

キーワード:
エラー学習する予備テストテストネガティブな暗示効果試験前の効果テスト効果true/false テスト

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科学分野:

  • 教育心理学
  • 認知科学
  • 学習科学

背景:

  • テスト前のテストとテスト後のテストは,学習の評価と改善のための一般的な教室のツールです.
  • テスト前とテスト後の比較上の利点に関する既存の研究は不確実です.
  • True/Falseテストは頻繁に使用されていますが,学習,特に誤った情報のリコールへの影響はよく理解されていません.

研究 の 目的:

  • 正確な情報を学習する上で真偽のプレテストとポストテストの効果を調査する.
  • 誤った情報の侵入率にプレテストとポストテストがどのように影響するかを調べる.
  • 誤った情報のリコールを最小限に抑えるために,プレテストとポストテストの有効性を比較する.

主な方法:

  • 学習成果を評価するために true/false テストを用いた3つの実験が行われました.
  • 参加者は知識の獲得と記憶の回復を評価するために 前テストと後テストを完了しました
  • 誤った情報の侵入率は正しい情報のリコールと並行して測定されました.

主要な成果:

  • テスト前のテストとテスト後のテストの両方で,正しい情報を学ぶための一貫した利点が示されました.
  • テスト前と比較して,誤った情報の侵入率が著しく高かった.
  • 本質的なフィードバックにより,誤った情報への侵入は全体的に減少したが,テスト前とテスト後の侵入率の違いは依然として有意であった.

結論:

  • 誤った情報を思い出すのを防ぐために,前試験は後試験よりも有効である.
  • フィードバックメカニズムは誤った情報のリコールを軽減しますが,事前テストは侵入を減らすのに明確な利点があります.
  • 教育戦略では,テスト前とテスト後の正確な記憶と不正確な記憶の違いが考慮されるべきです.