Avaneesh Srivastava’s Post

Confidence interval (CL) in statistics. A confidence interval (CI) in statistics is a range of values, derived from sample data, that is likely to contain the true value of an unknown population parameter. The interval has an associated confidence level that quantifies the level of confidence that the parameter lies within the interval. Common confidence levels are 90%, 95%, and 99%. Key Concepts 1. Point Estimate: A single value estimate of a population parameter (e.g., the sample mean). 2. Margin of Error (ME): The range above and below the point estimate, accounting for the uncertainty due to sampling variability. 3. Confidence Level: The probability that the confidence interval contains the true population parameter. For example, a 95% confidence level means that if we were to take 100 different samples and compute a confidence interval for each sample, we would expect about 95 of the intervals to contain the population parameter. #datascience #datascientist #dataanalyst #bianalyst #machinelearning #statistics

In statistical analysis, understanding the difference between critical values and p-values is crucial. Did you know a smaller p-value indicates stronger evidence against the null hypothesis? This insight can greatly affect how we interpret data. 🤓 How do you balance the findings you derive from these statistical concepts in your own research? https://lnkd.in/eig7K9Jz

New Win Vector statistics/analytics article (please share!): What Good is Analysis of Variance? https://lnkd.in/gGD4qp5E

From Beyond subjective and objective in statistics Decisions in statistical data analysis are often justified, criticized or avoided by using concepts of objectivity and subjectivity. We argue that the words ‘objective’ and ‘subjective’ in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity replaced by transparency , consensus, impartiality and correspondence to observable reality , and subjectivity replaced by awareness of multiple perspectives and context dependence. Together with stability , these make up a collection of virtues that we think is helpful in discussions of statistical foundations and practice. https://lnkd.in/eQH62K9R

5 Statistical Foundations that keep my work on track 👩💻 𝐒𝐚𝐦𝐩𝐥𝐢𝐧𝐠. Since it is often impractical or impossible to collect data from an entire population, statisticians rely on samples, which are a smaller subset of the population that is chosen to represent the whole. The principle of sampling ensures that the sample is selected in a way that is unbiased and accurately reflects the characteristics of the population. 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐔𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲. The main goal for statistics is to quantify uncertainty. Recognizing the constraints inherent in data and analysis, comprehending the range of potential outcomes. 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧 𝐯𝐬. 𝐂𝐚𝐮𝐬𝐚𝐭𝐢𝐨𝐧. Statistical analysis can reveal relationships between variables, but it's important to distinguish between correlation (a relationship between variables) and causation (where one variable directly influences another). 𝐊𝐞𝐞𝐩 𝐢𝐭 𝐒𝐢𝐦𝐩𝐥𝐞. Begin with simpler models first. Only introduce complexity when it's truly necessary. Complex models are prone to overfitting and can hinder understanding the data's underlying patterns. 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐜𝐞 𝐯𝐬. 𝐑𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝 𝐕𝐚𝐥𝐮𝐞. Not all surprising results are meaningful in practice. Just because something is statistically significant doesn't guarantee it makes a big difference in the real world. Consider how your findings apply to real-life situations to understand their true importance. #statistics #data #datingthescience #dataanalysis

Presentation of Data in Statistics

Demystifying Common Misconceptions about 🅿️Values in Statistical Analysis !!! It's crucial to navigate the intricacies of statistical analysis with clarity and accuracy. One key aspect that often leads to confusion is the interpretation of p-values. Let's debunk some common myths surrounding p-values: 🔹Myth 1: P value is not the probability that a null hypothesis is true or false. Instead, the p-value quantifies the probability of observing results as extreme as the ones obtained, assuming that the null hypothesis is true. This assumption is fundamental to the definition of the p-value. Without it, interpreting the p-value becomes meaningless. 🔹Myth 2: There is a magic value cutoff, like 0.05. The commonly cited p-value cutoff of 0.05 serves as a guideline rather than a rigid rule. Context, study design, and the desired level of certainty should all be considered when interpreting p-values. A p-value slightly above 0.05 doesn't automatically render results insignificant, nor does a p-value slightly below 0.05 guarantee significance. 🔹Myth 3: Small p-value means large effect size. While a small p-value indicates that the observed data is unlikely under the null hypothesis, it does not provide information about the size or practical significance of the effect. Even if something is statistically significant with a very low p-value, the effect size might be too small to be practically meaningful in the real world. Additionally, it's crucial to remember that p-values are not certainty. They represent probabilities, not guarantees. P-values are a tool, not the answer. They should be used in conjunction with other evidence and considerations to make informed decisions. In essence, p-values answer the question: Is your observed data sufficiently unusual under the assumption that the null hypothesis is true? However, it's essential to interpret p-values in conjunction with effect sizes and consider their practical implications. let's approach statistical analysis with a nuanced understanding of p-values, recognizing their role as a tool for inference rather than a definitive measure of truth. By dispelling these misconceptions, we can ensure more accurate and insightful interpretations of our analyses. #DataScience #Statistics #PValues #ResearchMethods #data

Discover what nominal data is and explore various nominal data examples. You’ll learn to collect and analyze this data type using best practices. #NominalData #CategoricalData #Statistics #DataAnalysis #SurveyData #DataVisualization #ChartExpo #ResearchMethods

An article that attempts to explain what P-Values mean in statistics in plain English without the technical jargon. https://lnkd.in/gZsKY6CU #Pvalue #statistics #sixsigma 

Hello everyone, please read my most recent blog, "Decoding Data: Unveiling the Mysteries of Complete and Sufficient Statistics". Do not forget to like, share, and subscribe to stay updated. #statistics #statisticsblog #datascience #blog #analysis #statisticalinference #inference #estimation https://lnkd.in/gBfwHdBz