Statistics Fundamentals: Statistics Fundamentals involves the basic concepts and principles of statistics, such as measures of central tendency, measures of variability, and probability theory. This skill is measured in the test to assess a candidate's understanding of the foundational concepts required for further statistical analysis.
Inference: p-values and confidence intervals: Inference: p-values and confidence intervals focus on statistical inference, which involves making conclusions or predictions about a population based on sample data. This skill is measured in the test to evaluate a candidate's ability to perform hypothesis testing and determine the significance or confidence level of statistical results.
Data Sampling: Data Sampling refers to the process of selecting a subset of observations from a larger population for statistical analysis. This skill is measured in the test to gauge a candidate's knowledge of different sampling techniques, understanding of sampling bias, and ability to effectively collect representative data.
Regression: Regression analysis is a statistical technique used to model and analyze the relationships between variables. This skill is measured in the test to assess a candidate's proficiency in fitting regression models, interpreting coefficients, and making predictions based on the model.
Sampling Distributions: Sampling Distributions involve the theoretical distributions of statistics calculated from different samples of the same population. This skill is measured in the test to evaluate a candidate's understanding of sampling distributions, including the central limit theorem and the concept of standard error.
Exploratory Data Analysis: Exploratory Data Analysis (EDA) involves the process of summarizing, visualizing, and interpreting data to gain insights and identify patterns or trends. This skill is measured in the test to assess a candidate's ability to perform data exploration techniques, such as plotting graphs, calculating summary statistics, and detecting outliers or missing values.
Non-parametric statistics: Non-parametric statistics are statistical methods that do not make any assumptions about the underlying population distribution. This skill is measured in the test to evaluate a candidate's knowledge and application of non-parametric tests, such as Mann-Whitney U test or Wilcoxon signed-rank test, when dealing with data that does not meet normality assumptions.
Probability: Probability refers to the measure of the likelihood of an event occurring, based on the ratio of favorable outcomes to the total possible outcomes. This skill is measured in the test to assess a candidate's understanding of probability theory, including conditional probability, independent events, and probability distributions.
Data Analysis: Data Analysis encompasses the process of inspecting, cleaning, transforming, and modeling data to discover useful insights and support decision-making. This skill is measured in the test to evaluate a candidate's proficiency in applying statistical techniques and interpreting results for practical data-driven problem-solving.
