Statistical Hypothesis Testing Puns

Hi guys. I’m hoping that I could get some suggestions on statistics textbooks.

I’ve previously worked in data analytics involving energy modelling, but I’ve since moved onto a career in engineering aligned with my degree. I’m currently seconded to an analytical engineering department, but (like with my previous job) I’m surprised how little statistical theory is actually used in the analysis that is done. I’m by no means a mathematician, but I’ve studied analytical engineering topics and have probably an above average interest in statistics for my profession. One of the reasons I was successful in my previous job was because I managed to show them the power of using more advanced statistical analysis techniques and R-based modelling, most of which I self-taught from various sources.

Basically, I want to put some of that into practice again, but I need a bit of a refresher (and supporting literature) since it’s been a while since I did this stuff. The topics I’m mainly looking at are:

Hypothesis Testing Experimental Analysis Modelling (Regression, or even Machine Learning) DoE (to a lesser extent)

Reference to R is a bonus too.

Thanks in advance.

I have a null hypothesis which states that there is no relationship between introduction of blockchain tech in companies and stock return volatility. My question is which test of significance should I use to decide if I should fail or not fail to reject the Ho.

Hypothesis: Healthy young-adults who score higher in the pre-aerobic-exercise cognition test will see statistically similar improvement to those who score lower.

Any help would be really appreciated because the only thing I can think of is separately analysing the lowest and highest x% in a repeated measure T-test and comparing the results?

Simply put, would this be acceptable or are there far better methods?

What should be the null and alternate hypothesis in this problem? Which test to apply? Is it a two-tailed test or left/right tailed test? Welp! Here it is

Hi everyone!

I'm a long time r/statistics lurker but I wanted to share something I have been working on for a couple of months – a web-based Hypothesis Testing Statistical Calculator. I would love to get any feedback/thoughts anyone might have. At the moment it only has options for one type of test (t-test with 2 independent samples), but I am planning to build it out for and more types of tests over time, for the sake of building my own knowledge if nothing else.

Background

I was previously working in a position for a few years which involved running and assessing A/B tests pretty regularly. As part of planning the experiments, we would typically estimate the required sample size for a given power and minimum effect size that would be of interest. To do these calculations, I would typically use G*power or just do the calculation myself in Jupyter. Once the experiment was done, I would also use Jupyter to assess the results.

Skip forward to two months ago, I have resigned my job, I am no longer using a Mac but instead have a Linux machine. I can't get G*Power (without also getting some emulation software) and I'm looking for a challenge, so I decide to build this tool to keep myself busy, build my knowledge of hypothesis testing and improve my coding.

TLDR: I got bored and built a web-based Hypothesis Testing Statistical Calculator, gimme feedback.

Hello everyone, I cannot seem to find this anywhere online, but I was wondering if researchers can use two different statistical tests for one hypothesis? For example, if I state something along the lines of:

The hypothesis predicted that higher reported personal contact with kittens would be correlated inversely to the CLS (cat lover scale by Purr and Meow, 2020/lower scores mean positive relationship with cats). For this hypothesis, a partial correlation was used to see if there was an association between affection scores from the CLS and kitten contact even when controlling for favorite pet bias. Additionally, an ANCOVA was used to see if mean scores on the CLS differed between those who have and have not yet interacted with a kitten even when controlling for social desirability.

Ok so first of all, and before people say "what the hell is the cat lover scale?" this isn't a real study and no cat lover scale exists. My question is, is the above procedure ok? Is it ok to use both a correlation AND an ANCOVA for one hypothesis? The addition of the ANCOVA would be to reveal more information about the relationship between kitty cat lovers and the cat lover scale.

By the way, dogs are better.

I'm ok in mathematics, probably two years or more past this subject. Except I'm going to have to teach someone which did not follow a mathematics curriculum, and it's entirely different than following the entire course and taking the exam myself. I only have the slides which shows what they're talking about but It's hard to infer what they're supposed to know..

Anyway, is there some good resources on (very)applied statistics? I probably shouldn't start with bare bone probability but I really want to. It's going to be clearer once I can talk to the student but I'm a bit anxious and knowing how it's taught in a book would help me.

edit : for example, I've skimmed through the 300 slides, and I'm pretty sure a lot of them would be easy to understand if you understand what a probability density function is. (At least, how to draw one for easy examples, understand how to interpret it, modify it etc..).

My thesis is about vehicle speed estimation from raw camera capture or video. My basic test is to compare if the vehicle's actual speed and my system's captured speed is the same or close. What i currently have are recorded speeds ranging from 15kph to 40kph only. What sort of statistical test should i perform? Im not even sure what sort of hypothesis I should be testing. Are there other sort of data gathering should I perform.? Pls help