Enhance Your Learning with Hypotheses Testing and Confidence Intervals Flash Cards for quick learning
A statistical method used to make inferences or decisions about a population based on sample data.
A statement that assumes there is no significant difference or relationship between variables in a population.
A statement that contradicts the null hypothesis and suggests that there is a significant difference or relationship between variables in a population.
Rejecting the null hypothesis when it is actually true, indicating a false positive result.
Failing to reject the null hypothesis when it is actually false, indicating a false negative result.
The probability of making a Type I Error, typically denoted as alpha (α).
The probability of obtaining a test statistic as extreme as the one observed, assuming the null hypothesis is true.
A range of values within which the true population parameter is estimated to lie, with a certain level of confidence.
A hypothesis test used to determine if the mean of a single sample is significantly different from a hypothesized value.
A hypothesis test used to compare the means of two independent samples and determine if they are significantly different.
A hypothesis test used to compare the means of two related samples, such as before and after measurements.
A statistical test used to determine if there is a significant association between two categorical variables.
Analysis of Variance, a statistical test used to compare the means of three or more groups and determine if there are significant differences.
A statistical method used to model the relationship between a dependent variable and one or more independent variables.
Certain assumptions that must be met for hypotheses testing to be valid, such as normality and independence of data.
The process of analyzing the output of a hypotheses test to draw conclusions about the population.
Errors or pitfalls that researchers often encounter when conducting hypotheses tests, such as misinterpreting p-values.
The distribution of a statistic, such as the mean or proportion, calculated from multiple random samples of the same size from a population.
A fundamental concept in statistics stating that the sampling distribution of the mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.
The level of confidence or probability that the true population parameter lies within the calculated confidence interval.
The systematic process of conducting a hypotheses test, including formulating hypotheses, selecting a significance level, collecting and analyzing data, and drawing conclusions.
The value that separates the rejection region from the non-rejection region in a hypotheses test, based on the chosen significance level.
The probability of correctly rejecting the null hypothesis when it is false, indicating the ability of a test to detect a true effect.
A measure of the strength or magnitude of the relationship or difference between variables in a population.
The likelihood that an observed result is not due to chance, typically determined by comparing the p-value to the significance level.
Real-world scenarios or research studies where hypotheses testing is applied to analyze and draw conclusions from data.
The process of estimating the range of values within which the true population parameter is likely to fall, based on sample data.
The difference between conducting a hypotheses test to make inferences about a population and estimating the value of a population parameter with a confidence interval.
The set of values of the test statistic that leads to the rejection of the null hypothesis, based on the chosen significance level.
The number of independent observations or pieces of information available for estimating a population parameter.
The application of hypotheses testing in various fields of research to test hypotheses, validate theories, and make evidence-based decisions.
The difference between a sample statistic and its corresponding population parameter, caused by random sampling variability.
A method of conducting hypotheses testing by comparing the test statistic to critical values and determining if it falls within the critical region.
The process of interpreting a confidence interval and understanding the range of values within which the true population parameter is likely to lie.
The use of hypotheses testing to ensure the quality and reliability of products or processes by comparing sample data to specified standards or benchmarks.
Methods used to select a representative sample from a population, such as simple random sampling, stratified sampling, and cluster sampling.
The application of hypotheses testing in medical research to evaluate the effectiveness of treatments, compare patient outcomes, and make evidence-based decisions.
The range of values covered by a confidence interval, indicating the precision or margin of error in estimating the population parameter.
The use of hypotheses testing in psychological research to study human behavior, test theories, and draw conclusions about psychological phenomena.
A systematic error or distortion in the sampling process that results in a non-representative sample, leading to biased estimates or conclusions.
The application of hypotheses testing in business and economics to analyze market trends, evaluate business strategies, and make data-driven decisions.
An extraneous variable that is related to both the independent and dependent variables, leading to a spurious or misleading relationship.
The use of hypotheses testing in social science research to study human behavior, social phenomena, and societal issues, and draw evidence-based conclusions.
The distribution of sample means calculated from multiple random samples of the same size from a population, which approximates a normal distribution.
The application of hypotheses testing in educational research to evaluate teaching methods, assess student performance, and make evidence-based decisions.
The level of precision or narrowness of a confidence interval, indicating the degree of certainty in estimating the population parameter.
The use of hypotheses testing in environmental research to study ecological systems, assess environmental impacts, and make informed decisions.
The distribution of sample proportions calculated from multiple random samples of the same size from a population, which approximates a normal distribution.
The application of hypotheses testing in engineering research to evaluate design alternatives, test materials, and make data-driven decisions.
The use of hypotheses testing in market research to analyze consumer behavior, evaluate marketing strategies, and make data-driven decisions.
The distribution of differences between two sample statistics calculated from multiple random samples of the same size from two populations, which approximates a normal distribution.
The application of hypotheses testing in sociological research to study social structures, analyze social phenomena, and draw evidence-based conclusions.
The use of hypotheses testing in political science research to study political behavior, analyze public opinion, and make evidence-based conclusions.
The distribution of ratios between two sample statistics calculated from multiple random samples of the same size from two populations, which approximates a normal distribution.
The application of hypotheses testing in financial research to analyze market trends, evaluate investment strategies, and make data-driven decisions.
The use of hypotheses testing in biological research to study living organisms, test hypotheses, and draw evidence-based conclusions.
The distribution of differences between two sample means calculated from multiple random samples of the same size from two populations, which approximates a normal distribution.
The application of hypotheses testing in healthcare research to evaluate treatment effectiveness, assess patient outcomes, and make evidence-based decisions.
The use of hypotheses testing in computer science research to evaluate algorithms, analyze data, and make data-driven decisions.
The distribution of differences between two sample proportions calculated from multiple random samples of the same size from two populations, which approximates a normal distribution.
The application of hypotheses testing in technological research to evaluate performance, test innovations, and make data-driven decisions.