Despite the importance of nonhuman primates, reference genomes have been sequenced in <10% of species.
A Science study presents high-quality reference genomes for 27 primate species, adding to available resources. https://t.co/vqD1A2xi6l #PrimateGenomes
Large language models (LLMs) have revolutionized the field of NLP , enabling machines to generate human-quality text, translate languages, and answer questions in an informative way.
💥 Here are 7 steps to master large language models: https://t.co/LUfBgIl8Gg
In two independent Science studies, researchers used time-resolved crystallography at an x-ray free electron laser to capture the process of #DNA repair from picoseconds to microseconds.
Learn more ⬇️
📄: https://t.co/jVqjQqgmnj
📄: https://t.co/uohRVczEVn
9 most common statistical tests used in A/B testing. Here's a breakdown:
A/B Testing is the biggest driver of conversion rate improvement (more than Customer Journey analysis, Segmentation, Surveys, and Cart Abandonment Analysis). That's why if A/B testing is NOT in your toolbox, you'd better get up to speed now. Let me help.
Here are the 9 most common statistical tests that beginners get tripped up with. And an explanation of when to use them:
1. Z-Test: Used with sample sizes, known population variance. Used to determine if there is a difference between two means. Often used for proportions like click-through rates.
2. T-Test (Student's): Smaller sample sizes, unknown population variance. Suitable for comparing means from two groups. Includes independent and paired samples t-tests.
3. Welch's T-Test: Unequal variances and/or sample sizes. An adaptation of the t-test that does not assume equal variances, offering more flexibility.
4. Chi-Squared Test: Categorical data, testing for independence or goodness of fit. Useful for assessing if there is a significant association between two categorical variables.
5. ANOVA: Comparing means of three or more groups. Ideal for understanding if there are any significant differences between the means of multiple groups.
6. Mann-Whitney U Test: Non-parametric alternative to Z-test / T-test for non-normal distributions. Comparing two independent groups, especially when the data is not normally distributed.
7. Fisher’s Exact Test: Small sample sizes, especially in 2x2 contingency tables. Used for examining the significance of the association between two kinds of classifications.
8. Regression Analysis: Relationship between a dependent variable and one/more independent variables. For more complex A/B tests to understand the impact of multiple variables on an outcome.
9. Pearson's Chi-Squared Test: Categorical data in contingency tables. Determines if there is a significant difference in the distribution of categories between groups.
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Need to learn A/B Testing to prepare for 2024?
Learning A/B testing can help you get a new job, get a promotion, and make a career transition.
Companies need this skill to improve online conversions, and profitability now more than ever.
I have a good news. I have a free workshop on A/B test in #R and #Python:
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#DataScience #DataAnalytics
Apoie o trabalho do #JotaCamelo via PIX: [email protected]
ou pelo site: https://t.co/V5QAl9CAKt
Também colabore compartilhando esta charge nas redes sociais ou em qualquer veículo de imprensa
Apoie o trabalho do #JotaCamelo via PIX: [email protected]
ou pelo site: https://t.co/PObW2cP4dP
Também colabore compartilhando esta charge nas redes sociais ou em qualquer veículo de imprensa
Quick Linux Tip 💡
Don't like manpages?
Query https://t.co/NfOwmCHo4y from the terminal and quickly access cheat sheet for a range of Linux commands!
curl https://t.co/TkPTYg4Vmk 👇