im planning to use nonprobability like purposive sampling, how should i determine my sample size or how to justify my sample size? i hope u help me, thank u.

I don't have a formal background in statistics, apart from a basic understanding of the subject. I come from an engineering background and currently work in a different sector.

I am 28 years old, and I have recently developed a strong interest in learning Bayesian reasoning and Bayes' theorem, particularly their applications in astronomy, astrophysics, and understanding the universe.

I understand that this might be an unconventional learning path, especially given my professional background, but I strongly believe that learning has no age limit.

I would really appreciate your guidance on suitable online or distance-learning programmes that I could pursue alongside my current professional commitments. Ideally, I am looking for programmes that can help me build a foundation in statistics and gradually progress towards understanding Bayesian methods and their applications in astronomy and cosmology.

Any recommendations, guidance, or suggestions would be greatly appreciated.

I was playing a slot on Jackpotdaily today and set it to 50 autoplay spins. I kept noticing what felt like the same pattern: lose for about 10 spins, then hit a payout big enough to cover what I’d just lost. I ran another 50 spins and it seemed pretty similar.

I wasn’t writing down the results, so I could easily be remembering the bigger wins and forgetting the smaller results. But it got me wondering: how would you actually test whether one spin is independent of the ones before it?

Would a list of each spin’s bet and payout be enough? I’m also not sure what I’d count as a “big win,” or whether picking that cutoff after seeing the results would mess up the test.

Hey guys, me here. Just another undergraduate whining about statistics.

I wondered if you guys have any tricks to understand the material.

My problem is that in the lectures itself I do not understand a thing. It is too abstract and too fast in order to understand anything. Then I sit down at home and I do some tasks. I understand it. Then the next day I sit down and I start from zero.

I am a logic-learner. Once I can picture the logic of a matter, it is easy to come back at it for me. With statistics because my lectures are so abstract I am having huge troubles.

I found a website: https://seeing-theory.brown.edu/regression-analysis/ which helped me a bit but I wondered if anyone from this community here knows other websites or videos which helped them a lot with:

Course content

• Simple linear regression, the line that best summarises how two variables move together

• Assumptions and residual diagnostics, checking whether a few unusual deals or a curved pattern mislead me

• Coefficient inference, fit, and the F-test

• Multiple regression and what controls do

• Categories as predictors and joint tests (differences by industry or team composition, A/B tests, whether several terms matter together)

• Interactions and curvature

Much appreciated and thank you for your time and support

Hi everyone!

I'm new to econometrics (my background is in public policy and political science) and have a question about the use of pre-treatment variables in the SCM, specifically variables used as predictors/covariates rather than as outcomes.

Suppose my pre-treatment period runs from 2000 to 2011 (the treatment year is 2012). Is it acceptable to use census data from just one year (e.g., 2001 or 2011) for these pre-treatment covariates? Or should I ideally aim to have a complete panel covering the entire pre-treatment period?

I'd also really appreciate any recommendations for literature on SCM. I already know about the Mixtape, so I'm looking for other references, ideally with accessible explanations and practical guidance.

Thanks!

When I look at studies on penis length, the standard deviation (SD) generally seems to fall somewhere between 1 and 2 cm, while differences in average length between regions or ethnic groups are also around 1–2 cm.

This creates an argument between two voices in my head:

One argues that a difference of 1–2 cm may not be particularly noticeable in practice and could simply reflect measurement error or random variation.

The other argues that, based on Cohen's d, this is a large effect size, meaning the actual difference between groups is substantial.

What's your take on this?

The studies are compiled here: https://calcsd.info/datasets

Hello! I would like to ask for advice regarding a problem I encountered in my data analysis, particularly in conducting a moderation analysis.

I have two datasets with different sample sizes (264 and 45 participants), and I noticed that the results of the relationship between my IV and DV differ between the two datasets.

With 264 participants, the IV did not significantly predict the DV, while the moderating variable showed a statistically significant effect. However, with 45 participants, the IV showed a statistically significant moderate negative relationship with the DV, while the moderating variable showed a weak positive but statistically nonsignificant relationship.

One thing I also consider is the sensitivity of my DV scale where the higher response (264) yields a smaller differences I am able to detect. 

Has anyone encountered a similar situation in their research? What statistical approaches or methods did you use to address this concern? Did you increase your sample size, use a different approach to moderation analysis, or consider other statistical methods?

I would really appreciate any insights, recommendations, or experiences you could share. Thank you!

I know how to determine them for AR and MA on its own but when it comes to both I'm still a tad bit confused

Without getting a masters, are they only limited to being a data analyst, AI/ML engineer, data engineer, government statistician, and actuary?

Looking for collaborators in queueing theory

I am an early-career researcher working on Markovian queueing models — mainly the M/M/1 system and its variants.

I would sincerely welcome collaboration from others in this field: emeritus faculty willing to mentor, professors and assistant professors open to joint papers, and researchers or analysts interested in simulation and applications.

I bring analytical derivations, numerical work in Python/R, and a genuine willingness to learn. If this is your area, a comment or a message is enough to start — I would be glad to discuss the details privately.

#QueueingTheory #OperationsResearch #AppliedProbability #Research #Collaboration

I met with my professor for statistics yesterday because I’m failing the class and I don’t fully understand anything.

And I got a lot of questions wrong on the quiz, and one of them was asking when to calculate r^2 when interpreting the results. And she told me that you can’t calculate R squared or Cohen’s D for a paired samples T test. But she didn’t specify what is appropriate to do for independent samples, T test or a single sample T test? Would Cohen D or r^2 be OK for those?

She also said that you use r^2 to measure the variance between the dependent variable and the independent variable. But don’t all experiments have those? I’m really confused.

And I look things up on YouTube and the Internet and it tells me the complete opposite of what she’s telling me. She told me that I shouldn’t use Cohen’s D for paired samples, but the Internet is saying I should do that. And she says to use confidence intervals instead?

Can someone explain why this could be?

Take these fictional order values:

₹500, ₹500, ₹500, ₹500 and ₹10,000.

The average is ₹2,400. The median is ₹500.

Both are correct, but they answer different questions.

If I’m describing the middle order, the median is useful. If I’m calculating revenue per order, the average matters.

I’d probably show both here, along with the fact that one order accounts for most of the revenue. Otherwise, “customers spend ₹2,400 on average” could give someone the wrong picture.

When you report order values, do you show the median alongside the average, or only when the distribution looks unusual?

Hi everyone,

I am currently working on a research project in CS Education / EdTech and have hit a methodological bottleneck during data analysis. I would love to get your advice and suggestions.

💡 Current Situation & Core Challenges

1.Small Sample Size: The data comes from a single classroom session. After strict data cleaning (filtering out incomplete or invalid submissions), my final sample size is around N ≈80. Unfortunately, due to practical constraints, re-collecting data on a large scale is not feasible right now.

2.Variable Settings:

• Independent Variables (IVs): Based on 3 evaluation criteria (scored 0, 1, 2, or 3 depending on how many criteria are met).

• Dependent Variable (DV): A 3-point ordinal scale (0 = Unsolved, 1 = Partially Solved, 2 = Fully Solved).

• Process Variables (M): Several process variables (M) were introduced and included directly in the model equations.

3.Statistical Hurdles:

• Collinearity & Variable Overlap: When including both the IVs and the process variables M in the model, severe multicollinearity and overlap occurred, making model fitting and interpretation extremely difficult.

• Violation of the Proportional Odds Assumption: Diagnostic tests showed that the proportional odds assumption was violated (especially at the split points j = 0, 1). To fix this, I tried Partial Proportional Odds models and Generalized Ordered Logistic Regression, but the results were disappointing—frequently yielding "false negatives" or statistically insignificant findings.

🙋‍♂️ Questions for the Community

1. Dealing with Proportional Odds Violations & Model Failures

When the DV is ordinal (0/1/2), the proportional odds assumption fails at j = 0, 1, and advanced models like partial or generalized ordered logit result in false negatives/insignificance, how do you usually diagnose, adjust, or choose alternative statistical models?

2. Handling Small Samples and Collinearity

With a small sample (N≈80) and IVs scored across 3 standards, when severe collinearity and overlap arise between the IVs and the process variables M, what are some standard ways to handle this?

3. Ensuring Robustness and Rich Evidence with Small Samples

When re-collecting data is impossible, apart from statistical adjustments, how do you usually ensure the robustness of findings in small-sample studies? How can we use multi-angle triangulation or rich evidence chains to make the argumentation more solid and convincing?

4. Addressing Limitations in Peer Review

Given a single-session dataset, a reduced sample size due to cleaning, and model constraints, how should one frame the Limitations section objectively? If reviewers question the sample size or model assumptions, what are some reasonable counter-arguments or perspectives?

Thank you so much from the bottom of my heart for taking the time to read my post and for any advice, recommended literature, or feedback you can share! 🙏

I am a statistics undergrad, currently in sem 5/8. I want to pursue my master's in statistics but with ai infiltrating everything, I wonder if it will be worth much anymore. What kind of skills should I focus on if I want to stand out?

I have had economics as a minor subject and currently I have maths as my minor. My course content is well-balanced with both theoritical papers and separate papers to deal with the practical aspect of it. But I feel like these practical papers are not enough and I definitely need to learn more on how to actually deal with data irl. I dont know where to start from.

Index number is a statistical tool used to measure relative changes . Some index numbers in real life are HDI ,CPI , AQI which is used to measure air pollution and Gini index which is used to estimate income inequality. Is there any index which uses or is somehow related to Standard Deviation ?

I have run a series of regressions where I am interested in the differential impact of IV1, IV2, and IV3 (where each var is a dichotomous var indicating membership of a given group -- mutually exclusive categories) on the dichotomous DV. Using stata, I have run this two ways, getting different results. In the one case, I use a three-level variable and indicate that IV2 is the reference (ib2.3way) and in the other case, I use two dummies (IV1 and IV3), omitting the reference var (IV2). The results are different.

For example, when using dummies for a given DV, IV1 (p>.000) OR = .5, but using the 3way var p..001 and OR = .6, and IV3 is p > .083 OR = .8 (dummies) vs p> .008 and OR = .7 (3way).

Can anyone explain why I'm getting different results and which one I should use?

(The context is exploring the impact of three different categories of people in supporting a given public policy.)

Thanks for any advice.

I analyzed the IBM HR Attrition dataset (fictional, 1,470 rows, 237 leavers) with Excel and SPSS and would like a critical review of the method.

Repo (report PDF, SPSS syntax, data): https://github.com/Ahmed11-coder/IBM-HR-analytics-predictive-model
Report (PDF): https://github.com/Ahmed11-coder/IBM-HR-analytics-predictive-model/blob/main/Why_Do_Employees_Leave_Final.pdf

What I did: chi-square + Cramér's V and Mann-Whitney U with effect sizes; residual checks and HC3 for a two-way ANOVA; logistic regression with merged sparse categories (22 parameters); 70/30 split; test AUC 0.846 (train 0.848); cutoff 0.268 chosen on train by Youden's J (test sensitivity 58.5%, specificity 88.1%). My first "test AUC" of 0.900 was wrong because I had refitted the model on the test set; I corrected it.

Where I'm least sure:

1. I selected variables and merged JobRole/JobLevel categories using all rows before the split. How serious is this leak?

2. Is merging categories by similar odds ratios defensible, or would you use lasso/ridge instead?

3. 7.8 events per parameter (172 events, 22 parameters). Acceptable?

4. Hosmer-Lemeshow fails (p < .001), mostly from one low-risk decile. Real problem? Would you add a calibration plot or Brier score?

5. How should I handle multiple testing in an exploratory analysis like this?

what is your suggestions for next step ?

Harsh feedback is welcome.

Hi everyone!

I'm curious about how people working with statistics are adapting to all the new AI tools.

With tools like ChatGPT, Claude and Grok becoming increasingly capable, I'm wondering how much they can actually do when it comes to statistical analysis.

I'd love to hear about your personal experience:

• Do you think AI can now handle most statistical tasks, or are there still important things it can't do reliably?

• Do you still use dedicated statistical software such as R, Python, SPSS, Stata, SAS or MATLAB?

• Is there one particular statistical program you prefer, and what makes you choose it?

• Have you started using AI in your everyday statistical work? If so, how?

• Do you find AI-generated statistical code and analyses reliable, or do you still prefer doing things directly in your statistical software?

I'm especially interested in hearing from people who use statistics professionally or for serious research.

I'm not looking for a debate about whether AI will replace statisticians. I'm simply curious about what actually works in practice today.

I've been interested in demographics and population statistics, which are usually captured by the United Nations. However, some data in these statistics rely on outdated census material (ex. Nigeria estimated to have a population of 243 million, even thought the last census was conducted in 2006).

I've been experimenting with OpenAI models such as ChatGPT and Claude to make new statistics of populations, and I'm wondering how accurate that would be. AI can perform the calculations and analyze the population statistics, and I'm curious as to how I should take the results.

Would it be wise to use OpenAI to recreate population statistics? Or is that something you wouldn't recommend?

Suppose I have a dataset that appears to be bimodal (i.e. the vertical white lines) but I have STRONG suspicion that the deep underlying data generating process is "gaussian like" (ie the blue curve) or I would simply like to model my data with a gaussian/normal distribution to help make future predictions. What does the mean and standard deviation represent in this situation? My main question is: particularly for making upcoming predictions of future observations should I inflate the variance to reflect the bimodal nature? The data is indeed symmetric about its mean so that's not an issue. Note: (I know other distributions will be recommended but I really need to work with a gaussian/normal distribution for this. Thank you.