#inferential

[inwarvictory]

someone explain two way ANOVA test to me like I’m a toddler lol

[fountainpenchess]

Inferential Confusion Questionnaire items and factor loadings from Inferential confusion in obsessive–compulsive disorder: the inferential confusion questionnaire by Araema et. al.

[gossippool]

AH!! i just followed you bc im seriously dp&w-pilled rn and i saw you also study soc + lit!!! S tier combo, im definitely nOt biased at ALL

also what kind of data analysis do u do! (im a nerd LOL) is it mostly quant?

HI oh my god another sociology and literature enjoyer?#?£??? and no ur right it's the best combo of all time this is like peak enlightenment i fear

also yeah my data analytics minor is mostly quantitative!! tbh i haven't done most of the courses in my minor yet. i'm saving that all for my final university year because it is a STRUGGLE i used to be so good at math but now i'm not lol. so sorry to burst ur bubble but i'm only taking this because it's helpful 😭😭 but idk i wish i did like doing stats. maybe when i finally get to it i'll start to enjoy it

[chocokatsicle]

i need sleep

[elephantbitterhead]

Probable tedium ahead, re: Hannah Crawford (again)

I thought of a slightly more charitable interpretation of that stupid Hannah Crawford claim from earlier. The least charitable version says the human brain -- for reasons that are unstated and remain entirely opaque -- cannot distinguish between experiencing an event in real life and experiencing it by reading/seeing/hearing a fictional account. This interpretation of Crawford is what motivates the twitter comment about her stance being insulting to those who have experienced being assaulted in real life -- the idea that experiencing an assault in reality is somehow the same as experiencing it via a fictional depiction.

This view, if it is her view, seems indefensibly dumb. Even if we just take the most superficial aspects of being assaulted -- e.g. being injured or experiencing physical contact with another person -- it's immediately clear that none of those things are happening to the person experiencing it via fiction. So it's obviously wrong in that regard. If her claim is that it's somehow ~*neurobiologically*~ the same, then she needs to do a lot more work to make this claim tenable & it's not clear that work can actually be done.

For example, is the idea that I, the fictional experiencer, am undergoing the same neurochemical events as the person being assaulted? This seems unlikely given how different our situations are, but OK, maybe. However, I feel quite sure we don't have the science to back this claim up -- we could get it, perhaps, but I don't think we have it to hand. Also, for the sake of argument, let's say I AM undergoing these same neurochemical events -- so what? What does that mean? Why does it matter? What reason do we have to think that any of that chemical activity even translates into a conscious experience of any kind? Lots of chemical events happen in the brain that don't map onto any kind of thought/feeling/etc.

In short, this is a not-great view that is pathetically underargued. It's also very counterintuitive so it's probably not what she means.

So let's try a slightly more charitable version of Crawford's claim. Perhaps she's saying that the human brain at some important (biological?) level does not distinguish real events from fictional events. That is, if I read a fictional account of a mountaineer struggling on the Eiger face, my brain parses this as a non-fictional account of someone struggling on the Eiger face. So it's not that I'm somehow having the same experience as the struggling mountaineer, like it would be in the less-charitable interpretation. Instead, here the claim is that in some ambiguous respect the brain cannot distinguish the real from the not real. When the brain encounters fiction it uniformly interprets it -- again for unstated reasons --as a story about a real person.

Based on Crawford's invocation of the amygdala, I can only imagine this is some kind of neurochemical/neurobiological claim. Perhaps at the level of conscious thought & belief I can tell the real from the not real, but my poor caveman-brain neurochemical system cannot. This strikes me as a weird and perhaps pointless assertion. I think we can all agree that in many cases people can distinguish fact from fiction with varying degrees of accuracy; there will be problem cases, as always, but we don't need to argue about whether that basic ability exists.

Given that we can clearly do that at the macro level, the question is becomes why the neurochemical level would matter at all. As with the previous interpretation of Crawford's claim, why do the associated neurochemical events have any relevance here? Again, let's generously allow that I experience the same neurochemical events whether I am witnessing a 'real' tragedy or merely reading about it. Is the idea supposed to be that I'm somehow going to blow out my amygdala? That I only get a fixed number of tragedy brain-reactions in my lifetime & I'm wasting them on fiction? This seems improbable & we certainly don't have evidence to support this claim.

Or is it that I'm somehow supposed to be hardening myself to real-life tragedy by repeatedly exposing my amygdala to the neurochemical events of fictional tragedy? So I'm not running out of tragedy brain-reactions, but I'm somehow making my brain chemically (??) less responsive to tragedy by overexposing it to tragedy reactions. This sounds to me like a strange claim (and again one that would require A LOT more scientific support). How do we get this crossover from the neurochemical to the level of thought & belief? We agreed earlier that we can usually tell the bulk of fact & fiction apart -- how does the amygdala blast start making this fall apart? Why does it fall apart in this direction -- that is, why is the result that I care LESS about real-world tragedy instead of just freaking out about fictional tragedy MORE? This view also seems to entail that people who experience a lot of tragedy firsthand should -- as they become desensitized -- react more indifferently to later tragedies, and intuitively it seems that the opposite is actually the case. Well, up until the point where they're just destroyed, obvs -- but that kind of destruction isn't what we see in people who read/see/etc. a lot of tragedy fiction. They're not sitting around hollow eyed, unconcerned about whether they live or die -- that's a different situation, although if it's all down to neurochemistry that's supposed to be the same in both cases, it seems like it shouldn't be. In addition, this version of Crawford's view also starts sounding perilously like the people who believe in the emotional-desensitization-via-fiction narrative, a view we know to be at odds with actual research in this area. What that shows us is that fiction often makes people MORE sensitive to these events (esp. in the lives of people unlike themselves), not less.

Anyhow, I cannot come up with a way to understand her claim that is not one of these two versions. Both are bad & I feel very confident that she has no solid argument or data to support either stance. Maybe that's why, rather than making any solid scientific assertions about the neurochemistry at issue, she only says 'What do you think this content is doing to our amygdalas?' -- because she doesn't know.

[kristinasjourney1988]

Help me with some social psychology research, please and thanks in advance!

Two question survey, totally confidential, you can see the results after!!!

[tutorchrome-k2-learn]

We will ensure that all work done by our writers can be submitted as one’s own writing

#collegeassignment #ProjectAssignment #homeworkhelp #assignmenthelp #followme #onlineassignment #liking #harvard #referencing #university #australia #usa #economicsexam

[tedkaczynskiofficial]

They can probably accurately guess about how many people are LGBT+ by comparing how the large number of people who explicitly state it in their bio use the site to users who don't explicitly state their sexuality somewhere on their blog.

LGBT+ people probably make way more than 25% of the posts here, but that doesn't mean cis/het people who like/reblog aren't users of the website. % of interaction on the site doesn't necessarily describe the actual demographic breakdown of users (it's still interesting/useful information, though).

I'm also going to point out that staff might actually not be super biased in their handling of reports, but that if only posts by trans people get reported that trans people are disproportionately going to be affected by their moderation. I'm sure there is some level of bias involved by the staff themselves, but seeing as we know TERFs and whatnot intentionally report trans people, it really would make the most sense for that to be where the majority of the disparate impact comes from.

Thanks for all of the recent feedback around Community Labels being incorrectly applied to content. In particular, we appreciate the input we’ve received from the LGBTQIA+ community and understand the frustrations from folks who felt that their content was unfairly labeled. When we realized this was happening, we immediately investigated and are taking steps to prevent this from happening again.

The LGBTQIA+ community makes up about a quarter of the Tumblr community. It is important for us to support all Tumblr users, especially those whose safe spaces are under threat in certain parts of the world.

As you know, alongside of the rollout of Community Labels we also expanded the types of content allowed on Tumblr as a way to welcome more creativity, art, and self-expression. Our goals remain the same today. Human error happens and we apologize to anyone who has been impacted by these mistakes.

We are working to better understand what happened and will follow up with more information soon.

[twentyseven-evil-bunnies]

Cohen’s D is the most useless thing I learnt. Wtffff

[mitcenter]

Descriptive vs Inferential Statistics: What Sets Them Apart?

Statistics is a critical field in data science and research, offering tools and methodologies for understanding data. Two primary branches of statistics are descriptive and inferential statistics, each serving unique purposes in data analysis. Understanding the differences between these two branches "descriptive vs inferential statistics" is essential for accurately interpreting and presenting data.

Descriptive Statistics: Summarizing Data

Descriptive statistics focuses on summarizing and describing the features of a dataset. This branch of statistics provides a way to present data in a manageable and informative manner, making it easier to understand and interpret.

Measures of Central Tendency: Descriptive statistics include measures like the mean (average), median (middle value), and mode (most frequent value), which provide insights into the central point around which data values cluster.

Measures of Dispersion: It also includes measures of variability or dispersion, such as the range, variance, and standard deviation. These metrics indicate the spread or dispersion of data points in a dataset, helping to understand the consistency or variability of the data.

Data Visualization: Descriptive statistics often utilize graphical representations like histograms, bar charts, pie charts, and box plots to visually summarize data. These visual tools can reveal patterns, trends, and distributions that might not be apparent from numerical summaries alone.

The primary goal of descriptive statistics is to provide a clear and concise summary of the data at hand. It does not, however, make predictions or infer conclusions beyond the dataset itself.

Inferential Statistics: Making Predictions and Generalizations

While descriptive statistics focus on summarizing data, inferential statistics go a step further by making predictions and generalizations about a population based on a sample of data. This branch of statistics is essential when it is impractical or impossible to collect data from an entire population.

Sampling and Estimation: Inferential statistics rely heavily on sampling techniques. A sample is a subset of a population, selected in a way that it represents the entire population. Estimation methods, such as point estimation and interval estimation, are used to infer population parameters (like the population mean or proportion) based on sample data.

Hypothesis Testing: This is a key component of inferential statistics. It involves making a claim or hypothesis about a population parameter and then using sample data to test the validity of that claim. Common tests include t-tests, chi-square tests, and ANOVA. The results of these tests help determine whether there is enough evidence to support or reject the hypothesis.

Confidence Intervals: Inferential statistics also involve calculating confidence intervals, which provide a range of values within which a population parameter is likely to lie. This range, along with a confidence level (usually 95% or 99%), indicates the degree of uncertainty associated with the estimate.

Regression Analysis and Correlation: These techniques are used to explore relationships between variables and make predictions. For example, regression analysis can help predict the value of a dependent variable based on one or more independent variables.

Key Differences and Applications

The primary difference between descriptive and inferential statistics lies in their objectives. Descriptive statistics aim to describe and summarize the data, providing a snapshot of the dataset's characteristics. Inferential statistics, on the other hand, aim to make inferences and predictions about a larger population based on a sample of data.

In practice, descriptive statistics are often used in the initial stages of data analysis to get a sense of the data's structure and key features. Inferential statistics come into play when researchers or analysts want to draw conclusions that extend beyond the immediate dataset, such as predicting trends, making decisions, or testing hypotheses.

In conclusion, both descriptive and inferential statistics are crucial for data analysis and statistical analysis, each serving distinct roles. Descriptive statistics provide the foundation by summarizing data, while inferential statistics allow for broader generalizations and predictions. Together, they offer a comprehensive toolkit for understanding and making decisions based on data.

[shakir2]

Is Islamic Traditionalism rational and relevant? 

It is understandable why some Muslims view the modern/Western academic study of Islam with suspicion, while others look down on traditionalist study as some relic of the past. Both mindsets are, at some level, justified, and both are reductionist. It is wrong to assert that it must be one or the other way, for both have pros and cons.  Traditional study is the bedrock of a Muslim’s Islamic…

[marketxcel]

5 Methods of Data Collection for Quantitative Research

Discover five powerful techniques for gathering quantitative data in research, essential for uncovering trends, patterns, and correlations. Explore proven methodologies that empower researchers to collect and analyze data effectively.

[thatstoomanysausages]

My psychology classmates should be thanking me on their knees for lowering the curve fr💪

[cohendyke]

not really sure how i’m feeling about spss to be honest idk….. i feel like i’m cheating on my main girl (excel)

[easternindiatechnosoft-blog]

Big Data Analysis Company in Kolkata

Introduction

In the dynamic landscape of technology, big data has emerged as a game-changer for businesses worldwide. As organizations in Kolkata increasingly recognize the importance of harnessing data for strategic decision-making, the role of big data analysis companies has become pivotal.

The Rise of Big Data in Kolkata

Kolkata, known for its rich cultural heritage, is also witnessing remarkable growth in the realm of big data. Over the years, the city has transitioned from traditional methods to advanced data analytics, keeping pace with global trends.

Key Players in Kolkata’s Big Data Scene

Prominent among the contributors to this transformation are the leading big data analysis companies in Kolkata. Companies like DataSolve and AnalytixPro have carved a niche for themselves, offering cutting-edge solutions to businesses across various sectors.

Services Offered by Big Data Companies

These companies provide a range of services, including data analytics solutions, machine learning applications, and customized big data solutions tailored to meet the unique needs of their clients.

Impact on Business Decision-Making

The impact of big data on business decision-making cannot be overstated. By analyzing vast datasets, companies can gain valuable insights that inform strategic decisions, leading to increased efficiency and competitiveness.

Challenges and Solutions

However, the journey toward effective big data implementation is not without challenges. Big data companies in Kolkata face issues like data security and integration complexities. Innovative solutions, such as advanced encryption algorithms and seamless integration platforms, are being developed to address these challenges.

Future Prospects

Looking ahead, the future of big data in Kolkata appears promising. The integration of artificial intelligence and the Internet of Things is expected to open new avenues for data analysis, presenting exciting possibilities for businesses in the city.

Case Study: Successful Big Data Implementation

A closer look at a successful big data implementation in Kolkata reveals how a major e-commerce player utilized data analytics to enhance customer experience and optimize supply chain management.

Training and Skill Development

To keep up with the evolving landscape, there is a growing emphasis on training and skill development in the big data industry. Institutes in Kolkata offer comprehensive programs to equip professionals with the necessary skills.

Big Data and Small Businesses

Contrary to popular belief, big data is not exclusive to large enterprises. Big data companies in Kolkata are tailoring their services to suit the needs of small businesses, making data analytics accessible and affordable.

Ethical Considerations in Big Data

As the volume of data being processed increases, ethical considerations become paramount. Big data companies in Kolkata are taking steps to ensure data privacy and uphold ethical standards in their practices.

Expert Insights

Leading experts in the big data industry in Kolkata share their insights on current trends and future developments. Their perspectives shed light on the evolving nature of the industry.

Success Stories

Success stories from businesses in Kolkata highlight the transformative power of big data. From healthcare to finance, these stories underscore the positive impact that data analysis can have on diverse sectors.

Tips for Choosing a Big Data Analysis Company

For businesses considering a partnership with a big data company, careful consideration of factors such as experience, scalability, and data security is crucial. Avoiding common pitfalls in the selection process is key to a successful partnership.

Conclusion

In conclusion, the journey of big data analysis company in Kolkata reflects a broader global trend. As businesses increasingly recognize the value of data, the role of big data analysis companies becomes indispensable. The future promises even greater advancements, making it an exciting time for both businesses and big data professionals in Kolkata.

Know more:

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[m0tel6mxzzy]

using a $4 psych stats textbook i found at a used bookstore to successfully teach myself the basics of statistics so i can apply it to my psych class