# Ch 1: Overview of Statistics

### About This Chapter

## Overview of Statistics - Chapter Summary and Learning Objectives

Let our knowledgeable instructors introduce you to the mathematical science used for research and experimentation in a variety of fields. Begin by learning how to decipher inferential from descriptive statistics and progress through the lessons to discover the uses of categorical and quantitative data or examine the traits of a randomized experiment. Lessons in this chapter are designed to teach you the following:

- Statistical data types
- Levels of measurement
- Model selection methods
- Sources of statistical bias
- Experiment design and analysis

Video | Objective |
---|---|

Descriptive and Inferential Statistics | Outlines the differences between descriptive and inferential statistics. |

What's the Difference Between Populations and Samples? | Shows how to determine when a figure represents a population and when it represents a sample, or subset, of a population. |

Defining the Difference Between Parameters and Statistics | Distinguishes between numerical summaries that are parameters and those that are statistics. |

Estimating a Parameter from Sample Data | Demonstrates steps for estimating parameters using statistics. |

What is Quantitative Data? - Definition & Examples | Illustrates when data is quantitative rather than categorical. |

What is Categorical Data? | Shows how to distinguish categorical data from quantitative data. |

Discrete and Continuous Data | Outlines the differences between discrete and continuous data. |

Nominal, Ordinal, Interval and Ratio Measurements | Identifies different levels of measurement. |

Evidence for the Strength of a Model through Gathering Data | Demonstrates the use of data to provide evidence of a model. |

Experiments vs. Observational Studies | Contrasts the characteristics of experiments and observational studies. |

Random Selection and Random Allocation | Illustrates the differences between random selection and random allocation and describes the benefits of each. |

Convenience Sampling: Definition and Limitations | Explains convenience sampling and the problems associated with its use. |

Designing Randomized Experiments | Outlines the traits of a good experiment. |

Analyzing and Interpreting the Results of Randomized Experiments | Demonstrates how to analyze data from randomized experiments and explains which experimental traits are necessary for making inferences. |

Confounding and Bias in Statistics | Provides strategies for recognizing statistical bias and confounding. |

Bias in Polls and Surveys | Explains common sources of bias in polls and surveys. |

Misleading Uses of Statistics | Illustrates some of the common ways statistics are used to mislead. |

### 1. Descriptive & Inferential Statistics: Definition, Differences & Examples

Descriptive statistics provide details about the given data, whereas Inferential statistics predict aspects of populations outside present data. Learn the application of both, and examples of the questions they are used to answer.

### 2. Difference between Populations & Samples in Statistics

A population includes all elements or members from a set of data while a sample is only a subset of a population. Learn about the differences between populations and samples and understand this difference's importance in collecting data.

### 3. Defining the Difference between Parameters & Statistics

A parameter is a measure that describes an entire population while a statistic is a measure that describes a sample from the population. Understand how to use statistics to understand populations, learn about population and samples, and differentiate parameters from statistics.

### 4. Estimating a Parameter from Sample Data: Process & Examples

Estimating parameter from sample data requires close analysis of the data collected from the population. Discover how statistics are used for understanding populations, learn the process of inferring parameters, and see some examples.

### 5. What is Quantitative Data? - Definition & Examples

Quantitative data allow researchers to answer questions that require counting and measurement. Learn about data that can be counted, data that can be measured, and uses of quantitative data.

### 6. What is Categorical Data? - Definition & Examples

Categorical data is a type of data that can be divided or classified into groups. Understand the definition and examples of categorical data, learn to distinguish categorical data from quantitative data, and explore the uses of categorical data.

### 7. Discrete & Continuous Data: Definition & Examples

Data refers to statistics and other types of information collected for research and analysis. Data comes in various types, including discrete and continuous. Explore the definition and examples of data to understand the different types of data available, and recognize how discrete and continuous data is collected and used.

### 8. Nominal, Ordinal, Interval & Ratio Measurements: Definition & Examples

The four levels of measurement of data are nominal, ordinal, interval, and ratio. Understand data and its importance, explore the definition and examples of the four levels of measurement, and learn how data types in each level differ.

### 9. The Purpose of Statistical Models

Statistical models use mathematical equations to formally describe the relationship of variables. Learn about statistical models and their purpose. Review the purpose of statistics, and explore the types of statistical models, as well as the types of variables, to understand how statistical models help explain variables and data.

### 10. Experiments vs Observational Studies: Definition, Differences & Examples

Experiments test the effect of one variable upon another via manipulation, whereas observational studies simply collect data according to what can be seen or heard. Learn the differences in method of each, and recognize the purpose in examples provided.

### 11. Random Selection & Random Allocation: Differences, Benefits & Examples

Radom selection is when a sample is selected from a group for a study, and random allocation is when members of that sample are selected for a treatment in an experiment. Study the differences and benefits of both study methods, and see an example.

### 12. Convenience Sampling in Statistics: Definition & Limitations

Convenience sampling involves selecting a research sample based on simplicity and proximity to the researcher. Identify some of the benefits to this common practice, as well as limitations that it presents compared to random sampling.

### 13. How Randomized Experiments Are Designed

Randomized experiments are designed by considering control elements and treatments with random sampling. Learn about experimental design, the controls and treatments of an experiment, and how to design an experiment.

### 14. Analyzing & Interpreting the Results of Randomized Experiments

Analyzing and interpreting the results of randomized experiments involves organizing data, and processing it with analytical tools according to the research goals. Learn how features of the randomized experiment inform this process and its results.

### 15. Confounding & Bias in Statistics: Definition & Examples

In Statistics, confounding refers to the problem of the study's structure, while bias pertains to the problem with the study itself. Discover the different issues in statistical analysis, the definitions of bias and confounding, and their examples.

### 16. Confounding Variables in Statistics: Definition & Examples

In statistics, confounding variables might interfere with the analysis of an experiment. Learn about faults in statistics, the definition of confounding variables, and also read about the placebo effect and lack of blinding in examples of each of these potential faults.

### 17. Bias in Statistics: Definition & Examples

Bias in statistics is where the results favor one group over the other in a study, which may or may not be done on purpose. Study the definition and examples of bias in statistics, as well as selection and response bias.

### 18. Bias in Polls & Surveys: Definition, Common Sources & Examples

Bias is anything that results in a data set that differs from the truth and can be influenced in various ways. Learn more about the definition, common sources, and examples of bias in polls and surveys.

### 19. Misleading Uses of Statistics

Statistics must be used carefully to avoid misrepresentation. Learn about misleading uses of statistics, including issues with sampling, surveys, data interpretation, limiting questions, leading questions, social desirability, misleading graphs, ranking issues, and qualifying issues.

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### Other Chapters

Other chapters within the Statistics 101: Principles of Statistics course