Statistics By Jim

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Data Collection Methods: Step-By-Step Guide with Examples

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What Are Data Collection Methods?

Data collection methods are organized processes for gathering observations and measurements to accurately answer research questions. Whether you study the environment, health, public opinion, or medicine, selecting the appropriate data collection methods ensures that your results are accurate and meaningful. For example, in environmental research, sound methodology helps scientists uncover valuable insights about ecosystems, pollution, wildlife, and climate change. [Read more…] about Data Collection Methods: Step-By-Step Guide with Examples

Positive Predictive Value: Meaning, Formula, and Interpretation

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What is Positive Predictive Value (PPV)?

Positive Predictive Value (PPV) assesses a diagnostic test’s accuracy by calculating the probability that a person who tests positive truly has the condition. PPV focuses on how trustworthy a positive result is in real-world testing scenarios. Hence, it is the best measure for interpreting an individual positive test result. Mammography, for example, is a well-known case where PPV plays a central role in understanding what a positive test result really means. [Read more…] about Positive Predictive Value: Meaning, Formula, and Interpretation

Median Absolute Deviation: Definition, Finding & Formula

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What is the Median Absolute Deviation?

The median absolute deviation is a measure of variability that indicates the typical distance between observations and the median. Unlike the mean absolute deviation, which uses the average, this method centers on the median, making it more resistant to outliers. The result uses the same units as the data, which helps with interpretation. Larger values signify that the data points spread further from the median, while lower values mean they cluster more tightly around it. Statisticians frequently abbreviate it as MAD, but sometimes use MADM to avoid confusion with the mean absolute deviation. [Read more…] about Median Absolute Deviation: Definition, Finding & Formula

Sensitivity vs Specificity: Definition, Formulas & Interpreting

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Sensitivity and specificity are two key metrics used to evaluate the performance of diagnostic tests or classification systems in statistics, medicine, and machine learning. These measures assess the intrinsic capabilities of a test. [Read more…] about Sensitivity vs Specificity: Definition, Formulas & Interpreting

Demand Characteristics in Psychology Studies

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What Are Demand Characteristics in Psychology?

Demand characteristics in psychology research are clues about a study’s research objectives. These clues give participants an idea of what the researchers hope to find and can cause them to change how they act or answer. Demand characteristics are only a concern in research involving human subjects. Hence, it’s a particularly big problem in psychology. It is a form of Response Bias. [Read more…] about Demand Characteristics in Psychology Studies

Factorial Design Explained: Testing Multiple Factors

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What is a Factorial Design?

A factorial design is an experimental design that simultaneously assesses more than one factor. By evaluating multiple factors at the same time, this design uncovers not only individual effects but also how factors interact. With this technique, each experimental run involves a random combination of factor values in a structured setting. [Read more…] about Factorial Design Explained: Testing Multiple Factors

Social Desirability Bias: Definition & Examples

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What is Social Desirability Bias?

Social desirability bias is the tendency for research participants to answer questions in a way that portrays them in a favorable light rather than providing completely honest responses. Typically, this bias occurs when participants answer questions to look better in the eyes of the researchers performing the study. It is a form of response bias, primarily affecting studies that use surveys and structured interviews to obtain self-reported information from the participants. However, it can occur in any study where the participants know researchers are watching. This bias reduces a study’s validity because the participants concealing their genuine opinions and behaviors. [Read more…] about Social Desirability Bias: Definition & Examples

Response Bias: Definition & Examples

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What is Response Bias?

Response bias occurs in studies when participants tend to provide inaccurate answers to questions. Societal norms and psychological factors can cause participants to systematically provide false responses. This research bias primarily affects studies that use surveys and structured interviews to obtain self-reported information from the participants. This bias reduces a study’s validity because the participants are concealing their true opinions and behaviors. [Read more…] about Response Bias: Definition & Examples

Inferential Statistics Definition & Examples

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What are Inferential Statistics?

Inferential statistics use samples to draw conclusions about populations. Typically, it is impractical to measure every population member. Instead, we collect a random sample from a small portion of the population, measure them, and use their data to estimate population properties. Using correct inferential statistics procedures, you can use samples to draw reasonable conclusions about whole populations. [Read more…] about Inferential Statistics Definition & Examples

Descriptive Statistics Definition and Examples

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What are Descriptive Statistics?

Descriptive statistics summarize the properties of a dataset using summary statistics, tables, and graphs. These descriptions characterize vital information about the variables, their relationships, and trends. Ideally, they provide a clearer picture of the data than the raw values. In short, they describe the essential features of a sample. [Read more…] about Descriptive Statistics Definition and Examples

Statistical Analysis Overview

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What is Statistical Analysis?

Statistical analysis involves assessing quantitative data to identify data characteristics, trends, and relationships. Scrolling through the raw values in a dataset provides virtually no useful information. Statistical analysis takes the raw data and provides insights into what the data mean. This process can improve understanding of the subject area by testing hypotheses, producing actionable results leading to improved outcomes, and making predictions, amongst many others. [Read more…] about Statistical Analysis Overview

Hypothesis Testing: Uses, Steps & Example

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What is Hypothesis Testing?

Hypothesis testing in statistics uses sample data to infer the properties of a whole population. These tests determine whether a random sample provides sufficient evidence to conclude an effect or relationship exists in the population. Researchers use them to help separate genuine population-level effects from false effects that random chance can create in samples. These methods are also known as significance testing. [Read more…] about Hypothesis Testing: Uses, Steps & Example

Missing Data Overview: Types, Implications & Handling

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Missing data refers to the absence of data entries in a dataset where values are expected but not recorded. They’re the blank cells in your data sheet. Missing values for specific variables or participants can occur for many reasons, including incomplete data entry, equipment failures, or lost files. When data are missing, it’s a problem. However, the issues go beyond merely reducing the sample size. In some cases, they can skew your results. [Read more…] about Missing Data Overview: Types, Implications & Handling

Data Aggregation: Strengths & Weaknesses of Aggregated Data

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What is Data Aggregation?

Data aggregation is a crucial process that involves collecting data and summarizing it in a concise form. This method transforms atomic data rows—sourced from diverse origins—into comprehensive totals or summary statistics. Aggregated data, typically housed in data warehouses, enhances analytical capabilities and significantly speeds up querying large datasets. [Read more…] about Data Aggregation: Strengths & Weaknesses of Aggregated Data

Prospect Theory Overview & Examples

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What is Prospect Theory?

Prospect Theory states that individuals place greater weight on losses than gains while making decisions. It is a descriptive model of how individuals make decisions involving risk and uncertainty proposed by Daniel Kahneman and Amos Tversky in 1979. Prospect theory describes how people evaluate and choose between different options. [Read more…] about Prospect Theory Overview & Examples