Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. It is used to understand and make decisions based on data.

What are the different types of statistics?

There are two main types of statistics: descriptive statistics and inferential statistics. Descriptive statistics are used to summarize and describe data, while inferential statistics are used to make predictions and draw conclusions about a population based on a sample.

What are some common statistical methods?

Some common statistical methods include regression analysis, hypothesis testing, analysis of variance (ANOVA), and chi-square tests.

What is the importance of statistics?

Statistics is important because it helps us make decisions based on data. It is used in many fields, including business, healthcare, and social sciences, to understand trends, patterns, and relationships in data.

What are some careers in statistics?

Some careers in statistics include data analyst, statistician, actuary, and market research analyst.

Statistics Forum - Learn statistics

At statistics.community, our mission is to provide a comprehensive platform for individuals and organizations to learn, share, and discuss all things related to statistics. We strive to create a welcoming and inclusive community that fosters collaboration and innovation in the field of statistics. Our goal is to empower our users with the knowledge and tools they need to make informed decisions and drive meaningful change through data-driven insights.

Video Introduction Course Tutorial

/r/statistics Yearly

📄 [E] StatQuest released a free map of his videos, organised into paths for learning

📄 [R] RStudio changes name to Posit, expands focus to include Python and VS Code

📄 [D] I'm so sick of being ripped off by statistics software companies.

📄 [E] FYI: Statistical Rethinking (2023) by rmcelreath

📄 [D] Job more challenging than university

📄 [Education] [PSA] [Rant] Don't you dare write or post about Gamma distributions without saying what parameterization you are using.

📄 As statisticians, do you guys remember a bit of everything in stats? How often do you have to review material? [D]

📄 [E] A nice chart of the most commonly used univariate distributions and their relationships

📄 [D] Why can't we say "we are 95% sure"? Still don't follow this "misunderstanding" of confidence intervals.

📄 [C] Are ML interviews generally this insane?

📄 Why don’t we always bootstrap? [Q]

📄 [C] What are your red flags for boring statistics/data science jobs?

📄 [D], [C] Statisticians that have left academics for the industry, how rigorous are you with your data now?

📄 [Q] Which statistical methods became obsolete in the last 10-20-30 years?

📄 [E] Motivating Example to (Benevolently!) Trick People into Understanding Hypothesis Testing

📄 [C] End of year Salary Sharing thread

📄 Accuracy of 3Blue1Brown’s video on Central Limit Theorem? [Q]

📄 [Question] I finished graduate school with a master's in statistics almost a year ago, and I haven't been able to find a good job

📄 [Q] I have a lottery ticket. if the odds of winning the lottery are 1/30million but the lotto reports that the winning ticket was sold in my town. As of this second with that news - From a true mathematical perspective- are my odds still 1/30Mill or 1/number of tickets sold in my town?

📄 [E] Why We Divide by N-1 in the Sample Variance Formula

📄 [Q] Cousin was discouraged for pursuing a major in statistics after what his tutor told him. Is there any merit to what he said?

📄 [E] Why Neural Networks Can Approximate Any Function (The Universal Approximation Theorem)

📄 [Q]Statistics is very cool. But why?

📄 [Q] My company uses terms like “10% error” and “90% confidence level” when describing a sampling protocol. These aren’t the right terms are they?

📄 [C] Why is statistical programmer salary in the USA higher than in Europe?

📄 [Q] What are your favorites statistics/probability articles?

📄 [Q] What are some must-have books for a data analyst/statistician in industry?

📄 [Research] Statistics on social-science statistics: "Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty" "These results call for greater epistemic humility and clarity in reporting scientific findings"

📄 [Q] What kind of stereotypes do you hear often when you tell people you are a statistician/data scientist?

📄 [Career] Biostatistician salary thread - are we even making as much as the recruiters who get us the job?

📄 I love statistical learning [D]

📄 [Q] Why isn't there a significance level of .02, .03, or .04 (or anything in between .01 and .05 for that matter)? Why using .05, .01, .001, etc.? Is there a statistical basis or just because of convention?

📄 [Q] Why is it more statistically accurate to round down if the preceding number is even and round up when preceding number is odd?

📄 [Q] How to convince my boss that stats are important?

📄 [Q] What is the probability such that, if shuffling a standard deck of 52 cards, there are no naturally occurring pairs?

📄 The Map of Statistics - Fun Video that Literally Maps Out The World [M]

📄 [Q] Bayesian vs Frequentist split

📄 [Question] What did the chief scientist at OpenAI mean by calling machine learning "just statistics on steroids"?

📄 Why don’t most statistics departments involve themselves with ML research [Q]

📄 [Q] Best way to learn SQL in the context of data analysis

📄 [E] I have recently have been part of the admissions process for my department's PhD program. AMA

📄 Statisticians who got their PhD and now work in industry, how is it like? [Q]

📄 [C] I screwed up and became an R-using biostatistician. Should I learn SAS or try to switch to data science?

📄 [Q] Are there any youtube video series which teaches statistics?

📄 [Q] / [D] People's silly ideas on statistics - how to talk with them

📄 [E] An Interactive Introduction to Statistics

📄 [Q] How hard is it to learn R for medical statistics?

📄 What actually is a kernel? [Q]

📄 [Q] What is wrong with interpreting a Bayesian probability from a Frequentist CI?

Statistics Cheat Sheet

Welcome to the world of statistics! This cheat sheet is designed to help you get started with the basic concepts, topics, and categories related to statistics. Whether you are a beginner or an experienced data analyst, this reference sheet will provide you with the essential information you need to know.

Introduction to Statistics

Statistics is the science of collecting, analyzing, and interpreting data. It is used to make informed decisions and draw conclusions about a population based on a sample. Statistics can be divided into two main categories: descriptive statistics and inferential statistics.

Descriptive Statistics

Descriptive statistics is the branch of statistics that deals with the collection, analysis, and presentation of data. It is used to summarize and describe the characteristics of a dataset. Descriptive statistics can be further divided into measures of central tendency and measures of variability.

Measures of Central Tendency

Measures of central tendency are used to describe the center of a dataset. The three most common measures of central tendency are:

Mean: The arithmetic average of a dataset.
Median: The middle value of a dataset.
Mode: The most frequently occurring value in a dataset.

Measures of Variability

Measures of variability are used to describe the spread of a dataset. The three most common measures of variability are:

Range: The difference between the maximum and minimum values in a dataset.
Variance: The average of the squared differences from the mean.
Standard deviation: The square root of the variance.

Inferential Statistics

Inferential statistics is the branch of statistics that deals with making inferences about a population based on a sample. It is used to test hypotheses and make predictions. Inferential statistics can be further divided into hypothesis testing and confidence intervals.

Hypothesis Testing

Hypothesis testing is used to determine whether a hypothesis about a population is true or false. The process involves setting up a null hypothesis and an alternative hypothesis, collecting data, and using statistical tests to determine whether the null hypothesis can be rejected.

Confidence Intervals

Confidence intervals are used to estimate the range of values that a population parameter is likely to fall within. The process involves collecting data, calculating a point estimate, and using statistical tests to determine the range of values that the population parameter is likely to fall within.

Probability

Probability is the branch of mathematics that deals with the likelihood of events occurring. It is used to make predictions and calculate the odds of certain outcomes. Probability can be divided into two main categories: theoretical probability and empirical probability.

Theoretical Probability

Theoretical probability is the probability of an event occurring based on mathematical calculations. It is used to make predictions about the likelihood of certain outcomes.

Empirical Probability

Empirical probability is the probability of an event occurring based on actual data. It is used to calculate the odds of certain outcomes based on past occurrences.

Data Analysis

Data analysis is the process of collecting, cleaning, and analyzing data to draw conclusions and make informed decisions. It involves several steps, including data collection, data cleaning, data exploration, and data visualization.

Data Collection

Data collection is the process of gathering data from various sources. It can be done through surveys, experiments, or observational studies.

Data Cleaning

Data cleaning is the process of removing errors, inconsistencies, and outliers from a dataset. It is important to ensure that the data is accurate and reliable before analyzing it.

Data Exploration

Data exploration is the process of analyzing and visualizing data to identify patterns and relationships. It involves using descriptive statistics and data visualization techniques to gain insights into the data.

Data Visualization

Data visualization is the process of presenting data in a visual format, such as charts, graphs, and maps. It is used to communicate insights and trends in the data.

Statistical Models

Statistical models are mathematical representations of real-world phenomena. They are used to make predictions and test hypotheses. Statistical models can be divided into two main categories: descriptive models and inferential models.

Descriptive Models

Descriptive models are used to describe the characteristics of a dataset. They can be used to identify patterns and relationships in the data.

Inferential Models

Inferential models are used to make predictions and test hypotheses about a population based on a sample. They can be used to determine whether a hypothesis is true or false.

Statistical Software

Statistical software is used to analyze and visualize data. There are several popular statistical software packages available, including:

R: A free, open-source programming language for statistical computing and graphics.
Python: A general-purpose programming language that can be used for data analysis and visualization.
SAS: A proprietary software suite used for data management, analysis, and reporting.
SPSS: A proprietary software suite used for statistical analysis and data management.

Conclusion

Statistics is a complex and fascinating field that is essential for making informed decisions and drawing conclusions about the world around us. Whether you are a beginner or an experienced data analyst, this cheat sheet provides you with the essential information you need to know to get started with statistics. Remember to always approach data with a critical eye and use statistical methods responsibly. Happy analyzing!

Common Terms, Definitions and Jargon

1. Statistics: The science of collecting, analyzing, and interpreting data.
2. Data: Information collected for analysis.
3. Descriptive statistics: Methods used to summarize and describe data.
4. Inferential statistics: Methods used to make predictions or draw conclusions about a population based on a sample.
5. Population: The entire group of individuals or objects being studied.
6. Sample: A subset of the population used to make inferences about the population.
7. Variable: A characteristic or attribute that can take on different values.
8. Categorical variable: A variable that can be placed into categories or groups.
9. Numerical variable: A variable that can be measured or counted.
10. Discrete variable: A numerical variable that can only take on certain values.
11. Continuous variable: A numerical variable that can take on any value within a range.
12. Nominal scale: A scale that uses categories or names to measure a variable.
13. Ordinal scale: A scale that uses categories or names to measure a variable, but also has a natural order.
14. Interval scale: A scale that measures a variable with equal intervals between values, but has no true zero point.
15. Ratio scale: A scale that measures a variable with equal intervals between values and has a true zero point.
16. Mean: The average value of a set of data.
17. Median: The middle value in a set of data.
18. Mode: The most common value in a set of data.
19. Range: The difference between the largest and smallest values in a set of data.
20. Standard deviation: A measure of how spread out the data is from the mean.

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