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- Educational Statistics: Inferential Statistics
Educational Statistics: Inferential Statistics
| Description: This quiz is designed to assess your understanding of inferential statistics in the context of educational research. | |
| Number of Questions: 14 | |
| Created by: Aliensbrain Bot | |
| Tags: education educational statistics inferential statistics |
What is the purpose of inferential statistics?
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To describe a population based on a sample.
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To make predictions about a population based on a sample.
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To test hypotheses about a population based on a sample.
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To estimate the parameters of a population based on a sample.
Inferential statistics allow researchers to make inferences about a population based on a sample, including testing hypotheses about the population.
What is the difference between a parameter and a statistic?
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A parameter is a measure of the entire population, while a statistic is a measure of a sample.
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A parameter is a fixed value, while a statistic is a random variable.
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A parameter is known, while a statistic is estimated.
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All of the above.
A parameter is a measure of the entire population, while a statistic is a measure of a sample. Parameters are fixed values, while statistics are random variables. Parameters are known, while statistics are estimated.
What is the central limit theorem?
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The central limit theorem states that the distribution of sample means approaches a normal distribution as the sample size increases.
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The central limit theorem states that the mean of a sample is equal to the mean of the population.
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The central limit theorem states that the variance of a sample is equal to the variance of the population.
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The central limit theorem states that the standard deviation of a sample is equal to the standard deviation of the population.
The central limit theorem is a fundamental theorem of statistics that states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.
What is a hypothesis test?
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A hypothesis test is a statistical procedure used to determine whether a hypothesis about a population is supported by the evidence.
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A hypothesis test is a statistical procedure used to estimate the parameters of a population.
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A hypothesis test is a statistical procedure used to describe a population.
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A hypothesis test is a statistical procedure used to make predictions about a population.
A hypothesis test is a statistical procedure used to determine whether a hypothesis about a population is supported by the evidence from a sample.
What are the steps involved in conducting a hypothesis test?
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State the null and alternative hypotheses.
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Collect data from a sample.
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Calculate the test statistic.
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Determine the p-value.
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Make a decision about the null hypothesis.
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All of the above.
The steps involved in conducting a hypothesis test include stating the null and alternative hypotheses, collecting data from a sample, calculating the test statistic, determining the p-value, and making a decision about the null hypothesis.
What is a p-value?
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The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the observed test statistic, assuming the null hypothesis is true.
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The p-value is the probability of rejecting the null hypothesis when it is true.
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The p-value is the probability of accepting the null hypothesis when it is false.
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The p-value is the probability of making a Type I error.
The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the observed test statistic, assuming the null hypothesis is true.
What is a Type I error?
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A Type I error is rejecting the null hypothesis when it is true.
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A Type I error is accepting the null hypothesis when it is false.
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A Type I error is making a false positive decision.
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A Type I error is making a false negative decision.
A Type I error is rejecting the null hypothesis when it is true, also known as a false positive decision.
What is a Type II error?
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A Type II error is rejecting the null hypothesis when it is false.
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A Type II error is accepting the null hypothesis when it is true.
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A Type II error is making a false positive decision.
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A Type II error is making a false negative decision.
A Type II error is accepting the null hypothesis when it is false, also known as a false negative decision.
What is the relationship between the significance level and the p-value?
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The significance level is the maximum p-value at which the null hypothesis can be rejected.
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The significance level is the minimum p-value at which the null hypothesis can be rejected.
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The significance level is the probability of rejecting the null hypothesis when it is true.
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The significance level is the probability of accepting the null hypothesis when it is false.
The significance level is the maximum p-value at which the null hypothesis can be rejected. If the p-value is less than or equal to the significance level, the null hypothesis is rejected.
What is a confidence interval?
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A confidence interval is a range of values within which the true population parameter is likely to fall.
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A confidence interval is a range of values within which the sample statistic is likely to fall.
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A confidence interval is a range of values within which the p-value is likely to fall.
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A confidence interval is a range of values within which the significance level is likely to fall.
A confidence interval is a range of values within which the true population parameter is likely to fall, with a specified level of confidence.
What is the relationship between the confidence level and the width of a confidence interval?
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As the confidence level increases, the width of the confidence interval decreases.
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As the confidence level increases, the width of the confidence interval increases.
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The confidence level and the width of a confidence interval are not related.
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The relationship between the confidence level and the width of a confidence interval depends on the sample size.
As the confidence level increases, the width of the confidence interval increases, because a wider range of values is needed to achieve the desired level of confidence.
What is a chi-square test?
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A chi-square test is a statistical test used to determine whether there is a significant difference between the observed and expected frequencies of a categorical variable.
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A chi-square test is a statistical test used to determine whether there is a significant relationship between two categorical variables.
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A chi-square test is a statistical test used to determine whether there is a significant difference between the means of two groups.
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A chi-square test is a statistical test used to determine whether there is a significant relationship between a categorical variable and a continuous variable.
A chi-square test is a statistical test used to determine whether there is a significant difference between the observed and expected frequencies of a categorical variable.
What is an ANOVA test?
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An ANOVA test is a statistical test used to determine whether there is a significant difference between the means of two or more groups.
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An ANOVA test is a statistical test used to determine whether there is a significant relationship between two or more categorical variables.
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An ANOVA test is a statistical test used to determine whether there is a significant difference between the observed and expected frequencies of a categorical variable.
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An ANOVA test is a statistical test used to determine whether there is a significant relationship between a categorical variable and a continuous variable.
An ANOVA test is a statistical test used to determine whether there is a significant difference between the means of two or more groups.
What is a regression analysis?
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A regression analysis is a statistical technique used to determine the relationship between a dependent variable and one or more independent variables.
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A regression analysis is a statistical technique used to determine the difference between the means of two or more groups.
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A regression analysis is a statistical technique used to determine the relationship between two or more categorical variables.
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A regression analysis is a statistical technique used to determine the difference between the observed and expected frequencies of a categorical variable.
A regression analysis is a statistical technique used to determine the relationship between a dependent variable and one or more independent variables.