Statistics Archives

The Challenges in Program Evaluation Research

What is program evaluation research? What types of program evaluations are there? Program evaluation refers to any situation where we’re interested in measuring the outcome of an event, which we refer to as a “treatment.” “Treatments” encompass academic interventions, social programs, political policies, fitness regimens, business tactics, clinical trials, and so on. Keep reading to learn about program evaluation research design and major challenges.

How Big Data Helps Cause and Effect Studies

How do cause and effect studies benefit from big data? How does big data eliminate causal research problems? To get the most out of big data, Seth Stephens-Davidowitz says you should focus on its four benefits. In Everybody Lies, he dives into one of those benefits: easy cause-effect analysis. Let’s look at the two ways big data makes cause-and-effect studies easier.

Misleading Statistics: Lying With Numbers

How do people mislead with statistics? What are some real-world examples of misleading statistics? Anyone with the will and a capable computer program can perform statistical analyses. This accessibility combined with the ease and speed of information sharing in our technology-oriented culture makes it easy for misleading statistics to make their way into our lives and inform our opinions or decisions. Keep reading to find out how you can avoid falling into the trap of misleading statistics.

Publication Bias in Research: Negative Findings

The 25 Cognitive Biases: The Lollapalooza Effect

What is publication bias in research? Why do researchers choose not to report negative findings? Publication bias occurs when the outcome of a research project influences the decision to publish it. Researchers and publishers may be more inclined to share positive research findings because they make for more attention-grabbing headlines. Here’s how publication bias can lead to inflated confidence in research findings.

The Power and Importance of Big Data Analytics

What is the importance of big data analytics? How powerful is big data? In Everybody Lies, Seth Stephens-Davidowitz argues that one of the powers of big data is that it allows you to zoom in on specific subsets of data. This allows new insights and new types of studies. Learn more about how important big data is to various studies.

The Limitations of Regression Analysis in Stats

What are the uses and limitations of regression analysis in statistics? What are some things you should keep in mind when reporting the results of regression analysis? Regression analysis is a powerful research tool as it allows researchers to isolate variables of interest. However, regression analysis can be dangerous if its results are misinterpreted or misreported. Here are a few pointers to keep in mind when interpreting regression analysis results.

Standard Deviation: Explained in Simple Terms

What is the standard deviation in statistics? What can the standard deviation tell about a data set? The standard deviation is descriptive statistics that measures the degree of dispersion in a given data set. More specifically, standard deviation tells us how far each value is from the mean. Here’s what we can glean from the standard deviation, explained in simple terms.

The 2 Crucial Disadvantages of Big Data

What are the disadvantages of big data? At what point does data get in the way? In Everybody Lies, Seth Stephens-Davidowitz warns that good data science isn’t just a matter of amassing a giant data set. When working with data, he says it’s important to keep data’s shortcomings in mind and not lose sight of the bigger picture. Read more so you can be aware of big data’s disadvantages.

Understanding the Limitations of Descriptive Statistics

What is descriptive analysis? How do descriptive statistics help us make sense of data? What is the main pitfall of descriptive statistics as a research tool? Descriptive statistics take information in a data set and condense it into a meaningful figure like an average or percentile. Descriptive statistics help us summarize and describe data, characterize relationships, and make predictions. While descriptive statistics can help us make sense of data, they should be used with caution: Descriptive statistics tell us what happened, but they don’t necessarily tell us why. Keep reading to learn about the limitations of descriptive statistics.

Calculating Investment Risk Using Probability

How do you calculate investment risk? What do you need to know to calculate the expected payoff of a financial investment? Investors often use probability to assess risk when making financial decisions. This is typically done with a statistic called an “expected value.” To calculate the expected value of a financial investment, you need to know the probability of each possible outcome and its respective payoff. Here’s how to use the expected value statistic for calculating investment risk.