Research Vocabulary

#321

interval estimate

Example sentences

1. An interval estimate gives a range of likely values.
2. The confidence interval is a common type of interval estimate.
3. The team preferred an interval estimate because it shows uncertainty.

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#322

probability

Example sentences

1. The probability of drawing a red card was one half.
2. Probability values range from zero to one.
3. The model estimated the probability of recovery for each patient.

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#323

p-value

Example sentences

1. The analysis produced a p-value of 0.03.
2. The team compared the p-value with the chosen significance level.
3. A p-value alone does not show whether an effect is important.

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#324

significance level

Example sentences

1. The researchers set the significance level at 0.05 before the analysis.
2. The significance level shows the chosen risk of a type I error.
3. The p-value was compared with the significance level.

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#325

statistical significance

Example sentences

1. The researchers examined statistical significance when interpreting the results.
2. The report explains why statistical significance matters for the conclusion.
3. Statistical significance was considered together with effect size and uncertainty.

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#326

practical significance

Example sentences

1. Practical significance was considered together with effect size and uncertainty.
2. The analyst gave a simple explanation of practical significance.
3. The team discussed the difference between practical significance and other results.

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#327

effect size

Example sentences

1. The effect size showed a moderate difference between the groups.
2. The researchers reported the effect size with the p-value.
3. A large sample can produce significance even when the effect size is small.

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#328

statistical power

Example sentences

1. Statistical power is the chance of finding a real effect.
2. A larger sample can increase statistical power.
3. The team checked statistical power before starting recruitment.

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#329

type I error

Example sentences

1. The report explains the risk of a type I error.
2. A larger sample may reduce the chance of a type I error.
3. The analyst considered both kinds of error, including a type I error.

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#330

type II error

Example sentences

1. The analyst considered both kinds of error, including a type II error.
2. The study design affects the chance of a type II error.
3. The researchers tried to avoid a type II error in the analysis.

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#331

one-tailed test

Example sentences

1. The researcher used a one-tailed test because the prediction had one direction.
2. A one-tailed test looked only for an increase in the outcome.
3. The analysis plan stated the one-tailed test before the data were examined.

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#332

two-tailed test

Example sentences

1. The team used a two-tailed test because the result could move in either direction.
2. A two-tailed test can detect both an increase and a decrease.
3. The report explains why a two-tailed test was more appropriate.

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#333

parametric test

Example sentences

1. The analyst used a parametric test after checking the data distribution.
2. A parametric test often depends on assumptions about the data.
3. The report names each assumption required by the parametric test.

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#334

nonparametric test

Example sentences

1. The analyst chose a nonparametric test because the data were strongly skewed.
2. A nonparametric test can be useful for ranks or small samples.
3. The report compares the nonparametric test with the parametric result.

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#335

correlation

Example sentences

1. The correlation showed that study time and test scores increased together.
2. A strong correlation does not prove that one variable causes the other.
3. The report gives the correlation and its confidence interval.

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#336

covariance

Example sentences

1. The positive covariance showed that the two variables moved in the same direction.
2. Covariance was negative when one value rose as the other fell.
3. The analyst calculated covariance before building the model.

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#337

regression

Example sentences

1. The team used regression to study the relationship between income and health.
2. Regression can estimate how an outcome changes with a predictor.
3. The analyst checked the regression results for unusual values.

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#338

linear regression

Example sentences

1. The analyst used linear regression to predict a continuous test score.
2. Linear regression fitted a straight-line relationship to the data.
3. The report gives the main coefficient from the linear regression.

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#339

logistic regression

Example sentences

1. The team used logistic regression to predict a yes-or-no outcome.
2. Logistic regression estimated the chance of hospital admission.
3. The report presents odds ratios from the logistic regression.

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#340

multiple regression

Example sentences

1. Multiple regression included age, income, and education as predictors.
2. The analyst used multiple regression to examine several variables together.
3. The report explains how the multiple regression model was checked.

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