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JUReject them the way companies reject your cv’s 😂
#trend #fypp #fyp #viral #viralreels #pvalue #nullhypothesis #reject
@justtscroollll
#Null Hypothesis
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CHNull Hypothesis (H₀): This is the default assumption that there is no effect or no relationship between variables. It’s what we test against. Think: “Nothing’s happening here!”
Alternate Hypothesis (H₁): This is what we propose when we believe there’s an effect or relationship. It’s the claim we’re trying to prove. Think: “Something’s definitely happening!
#simplystatistics #psychology #research #psychologyfacts #nullhypothesis #alternatehypothesis #research #dissertation #chithappens
@chithappens.co
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TEHypothesis Testing:- 🧠 Understanding Hypothesis Testing in Data Science & Machine Learning Project ( Before deployment the Project )
Hypothesis Testing is a fundamental concept in statistics that helps us make data-driven decisions. Whether you're validating assumptions or comparing models, this tool is essential.
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🔍 What is Hypothesis Testing?
It’s a statistical method to test an assumption (hypothesis) about a population parameter using sample data.
✅ Key Terms You Must Know:
📏1. Null Hypothesis (H₀)
The assumption we start with — e.g., “There is no difference between A and B”
🎯 2. Alternative Hypothesis (H₁)
What we’re trying to prove — e.g., “There is a significant difference between A and B”
📉 3. p-value
Probability of observing the data if H₀ were true.
Low p-value (< 0.05) → Reject H₀
High p-value → Fail to reject H₀
🔁 4. Significance Level (α)
Threshold (usually 0.05) to compare with the p-value.
5. Type I Error (False Positive)
Rejecting H₀ when it’s actually true.
🔁 6. Type II Error (False Negative)
Failing to reject H₀ when H₁ is actually true.
📈 Common Tests:
t-test :- Compare means between two groups
z-test :- Known population variance and large sample
Chi-square test :- Categorical data relationships
ANOVA :- Compare means across 3+ groups
Shapiro-Wilk / KS test :- Check for normality
🧪 Example:
Problem: Does a new marketing strategy increase sales?
H₀: No change in sales
H₁: Sales increased
If p < 0.05, we reject H₀ and conclude the strategy likely worked 🎯
🔗 Why It Matters in ML?
~ Feature selection
~ Model assumptions (e.g., residuals in linear regression)
~ A/B Testing for product changes
~ Validating trends or business hypotheses
📌 Takeaway: Hypothesis testing is more than a formula — it's a mindset for making confident, data-backed decisions.
🧠 Choosing the Right Hypothesis Testing for your goal.
#HypothesisTesting #ab #datascience #interviewtips #dataanalytics #datasciencejobs #machinelearning #problemsolvings
@tech_jroshan
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_KMost of my followers either got this wrong or refused to answer the question. We don't accept the null hypothesis in any circumstances. We either fail to reject or retain if we cannot reject it. References below: Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49(12), 997–1003. https://doi.org/10.1037/0003-066X.49.12.997Fisher, R. A. (1935). The design of experiments. Oliver & Boyd.Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 231(694–706), 289–337. https://doi.org/10.1098/rsta.1933.0009
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#statistics #research #psychology #knightofsteel
@_knightofsteel
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AHWhen the p-value is less than 0.05, and you know what that means.
There I was, staring at my statistical analysis output, rereading the regression table, checking the research methodology, making sure the data analysis was correct. The moment every PhD student and graduate student waits for — statistical significance.
Reject the null hypothesis.
This is the real graduate school experience — research design, hypothesis testing, quantitative research, statistical analysis, and academic writing that define doctoral training and higher education. Moments like this remind you why research matters.
As a woman in STEM and an international student navigating academia, these small wins in dissertation research, data analysis, and scholarly work build confidence and resilience.
PhD life. Graduate school. Women in STEM. Academic journey. International student. Higher education. Doctoral journey. 🕊️🧕🏻
study abroad, international student, PhD journey, master’s abroad, student life in the USA, academic journey, research life, women in education, chasing dreams, growth phase, learning and growing, Keywords, becoming her, student journey, work, goals, memories, PhD life, PhD student, study, grad life, master’s abroad, study abroad life, international student journey, academic goals, growth, mindset, resilience, becoming her, dream life, that girl, studying, hard work, women in stem, university, grad school,
#studystudystudy #phdlife #studyroutine #gradstudent #usa
@ahyderabadiinusa
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THA p-value tells us how likely it is that our results happened just by chance.
When p > 0.05, the result is not statistically significant. This means the data does not provide strong enough evidence to reject the null hypothesis. The observed effect could be due to chance, but this does not mean the effect doesn’t exist, only that the study wasn’t convincing enough to demonstrate it.
Important: it does not prove that nothing is happening. It only tells us that the data isn’t convincing enough yet.
#psychology #trendingnow #college #fyp #viralvideos
@thesoulvia
583.0K
CHA p-value, or probability value, is a statistical measurement that indicates how likely it is that a set of data could have occurred under a null hypothesis
What it measures
The probability that the observed results could have occurred if the null hypothesis were true
What it indicates
A smaller p-value indicates greater statistical incompatibility between the data and the null hypothesis
#simplystatistics #psychology #research #statistics #dissertation #psychmajor #mapsychology #pvalue #psychologyfacts
@chithappens.co
572.3K
STstaying true to my username
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Statistics is the foundation of data analysis and inference across many disciplines. In hypothesis testing, statistics provides the rigorous framework for using sample data to make objective decisions about a population. This involves formulating a null hypothesis (H_0) and an alternative hypothesis (H_a), calculating a test statistic (like t-score or Z-score), and determining a p-value to assess the statistical significance of the evidence against H_0. In Machine Learning (ML), statistics is essential for tasks like Exploratory Data Analysis (understanding data distribution and variability), feature selection, and especially model evaluation (using metrics, confidence intervals, and hypothesis tests to compare models and validate predictions). For Time Series Analysis, statistical methods like ARIMA (Autoregressive Integrated Moving Average), moving averages, and autocorrelation are used to decompose data into components like trend, seasonality, and residual, enabling the identification of underlying patterns and robust forecasting of future values. Beyond these, statistics plays a crucial role in areas like experimental design, quality control, and risk assessment by quantifying uncertainty and providing reliable, data-driven conclusions.
This is not my content. All credits to the owner. Dm for credit / removal
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#math #statistics #computerscience #stats #cs #mathmemes #mathedits #statsandcs
@statcsmemes
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BANull hypothesis in simplified version
#research #nullhypothesis #reelsinstagram #ugcnetpaper1 #explore
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@rowrowlearn
@testbook_ugc_net
@bamel213
340.6K
SHThis video is not about whether OJ Simpson was guilty or innocent. That’s not what I’m here to debate.
I’m trying to show you something deeper. Something that affects you every single day. The most dangerous statistics aren’t fake. They’re real. They just answer the wrong question. And our brains aren’t built to catch that.
That’s the prosecutor’s fallacy. A number that’s technically correct but pointed at the wrong target. It doesn’t lie to you. It lets you lie to yourself. ⚖️
For example: A test is 95% accurate. You test positive. You think I’m probably sick. But if the disease is rare, most positive results are false positives. The number didn’t change. The question did.
A Harvard study gave doctors this exact problem. Most got it wrong. 🏥 (Will make a video on this later)
That’s why the OJ case is such a stong example.
One statistic shaped how 150 million people saw the evidence. Mathematician John Allen Paulos called it “astonishingly irrelevant.” Years later, a juror apologized to Nicole’s sister after learning the number they never heard.
Numbers don’t lie. They just answer whatever question you point them at. Point them wrong, and you get a perfect answer to something that was never the point.
That’s not a math problem. That’s a human problem.
Most of us hated math in school because it felt like memorizing formulas for no reason.
My goal is to help you build intuition ✨ instead and maybe fall in love with it again by showing how beautiful it is and how quietly it shapes your life. If you like content that makes math feel human, stick around and follow me 🙏
Who’s the best at using stats to lie?
@shubh856
13.3K
ANHypothesis testing is a statistical technique used to find evidence to support a certain assumption about a population based on sample data.
Below are the steps performed in Hypothesis Testing:
✅ State your hypothesis: Hypothesis is an assumption you make about data. We generally state both Null hypothesis and Alternative Hypothesis at this stage.
Null hypothesis is an assumption that there is no difference or no effect within the population. This is the assumption we are testing against.
Examples of Null hypothesis:
The average marks scored by students in a particular test is 75.
There is no difference between the average marks scored by students in test 1 and test 2
Alternative Hypothesis: This is an assumption we are trying to find evidence for. This is usually opposite of Null Hypothesis:
Examples of Alternative Hypothesis:
The average marks scored by students in a test is greater than 75.
There is a difference between average marks scored by students in test 1 and test 2. (Mean of test1>Mean of test2)
✅ Choose your significance level: This is generally 0.05 or 0.01
✅ Choose an appropriate test: Based on the type of data, you can choose one of the tests like T-Test, F-test, Chi-square test or ANOVA
✅ Calculate test-statistic and determine p-value: P-value is a probability which determines the strength of your evidence against Null Hypothesis.
✅ Make Conclusions: If p-value is less than or equal to the chosen significance level, then we reject Null hypothesis and conclude that there is a statistically significant difference between both the samples. However, if the p-value is greater that the chosen significance level, then there is simply not enough evidence to reject Null hypothesis.
{ Data Analyst, Statistics for Data Analysis, Teaching statistics for Data Analysis, Data science, Statistical analysis, Data scientist, Inferential statistics}
@analytics_essentials
2.4K
PSA p value tells us how likely it is that our results happened just by chance.
The null hypothesis functions as a reversal or negation of the research hypothesis. The researcher either rejects or fails to reject the null hypothesis, but never accept the null hypothesis. The entire goal of the research becomes to verify and reject the null hypothesis and in turn accept the alternative hypothesis.
When p<0.05, the result is statistically significant. This means the data provides strong evidence to reject the null hypothesis. It simply means that the result is very unlikely to be due to chance, so something real is happening. For example, if p=0.01, it means there is only 1% chance that this result happened randomly. Hence, there is a strong evidence against the null hypothesis. Thus, we reject the null hypothesis and accept the alternative hypothesis.
When p>0.05, the result is not statistically significant. This means the data does not provide strong enough evidence to reject the null hypothesis. For example, if p=0.50, it means that there is a 50% chance that this result happened randomly. The observed effect could be due to chance, but this does not mean the effect doesn’t exist, only that the study wasn’t convincing enough to demonstrate it. The evidence to reject the null hypothesis is weak and hence we fail to reject the null hypothesis. Remember, we never accept the null hypothesis.
[ statistics, psychology, null hypothesis, alternative hypothesis, research, p value, type I error, type II error, false positive, false negative, significance level, error, result, statistically significant, chance, random, weak evidence, strong evidence, accept, reject, fail to reject, research hypothesis, hypothesis, low p value, high p value, trending audio, relatable, viral, fyp, reels, instagram, stats, reel it feel it, trending reels ]
#relatable #statistics #psychology #viral #research
@psych.with.dee
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