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Which category of statistics is used to analyze quantitative data using measures like mean, median, and mode?
Which type of test is used to make inferences about a population based on sample data?
Which measure of central tendency represents the 'typical' value in the dataset?
What type of statistics are commonly presented using tables, graphs, charts, frequencies, or proportions?
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Which measure of spread tells us how close data points are from one another and how close they are to the mean/median?
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Which type of statistical test is based on making assumptions about the population parameters?
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Which measure best represents the middle value in a data set and is recommended for reporting in skewed data?
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What is the measure of how closely data points are packed around the mean, calculated by taking the square root of variance?
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What is a statement of expected relationships between variables or phenomena in hypothesis testing?
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In scientific research, what does a 5% significance level indicate?
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What type of error occurs when the null hypothesis is false but is inaccurately accepted?
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What type of data cannot be measured on a numerical scale and can be classified into distinct categories?
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What is used to determine whether the results from a sample can be extrapolated to the population of interest and to examine relationships or differences between variables?
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What measure represents the most frequently occurring value in a data set?
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'Type 1 error (alpha)' occurs when:
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'P-value' or statistical significance represents:
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What does 'Standard deviation (SD)' measure?
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What type of error occurs when the null hypothesis is true but is inaccurately rejected?
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What scale is used for quality of life ratings?
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What type of data are parametric tests used for?
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What does a negative correlation value indicate?
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Which correlation coefficient is used for non-parametric data?
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What does a significant negative correlation indicate?
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What is used to assess normality of data?
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When are non-parametric tests typically used?
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What does the p-value being less than 0.05 indicate?
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What does a positively skewed distribution have?
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What type of test is used for skewed data?
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What range do correlation values span from?
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Study Notes
- Quality of life ratings use a scale of 1 to 4, with 1 being poor and 4 being excellent, but the intervals are unknown and may not be equal.
- Parametric tests are used for normally distributed data, while non-parametric tests are used for skewed data.
- A normal distribution is a symmetrical distribution where most data points cluster around the mean. Skewed data have a mean that is larger (positively skewed) or smaller (negatively skewed) than the median.
- For parametric tests, assumptions include numerical data (interval or ratio variables) and a normal distribution.
- Non-parametric tests are used for categorical data and non-normal distributions, and are typically used for small sample sizes.
- Normality of data can be assessed using histograms, normality plots, or normality tests such as the Kolmogorov-Smirnov test or Shapiro-Wilk test.
- When selecting a statistical test, consider the objectives of the study, with some tests used to determine statistical significance, some to examine correlations, and others to compare groups.
- Pearson's correlation coefficient is used to measure the strength and direction of the relationship between two continuous variables in parametric data.
- Spearman's correlation coefficient is used for non-parametric data and measures the relationship between two ranked variables.
- Correlation values range from -1 to +1, with negative values indicating a negative relationship, and positive values indicating a positive relationship. The closer the value is to 1, the stronger the correlation.
- The study described in the text aimed to assess the association between the severity of side effects of oral antidiabetic medicines and non-adherence among patients with type 2 diabetes. The authors reported a significant negative correlation between the two (r = 0.20, p < 0.05).
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Description
This quiz covers the concept of hypothesis testing, including understanding, types of inferential statistics (parametric and non-parametric tests), and knowing when to use various statistical tests in research. It also includes a recap of the research process, identifying the need for research, developing a research question or hypothesis.