Inferential Statistics

"With this logic I could ask 10 random people and it would remain a true percentage." - the accuracy depends on how many people you ask, rather than how many people there exist in total. Asking 10 people will give you the same very low accuracy regardless of whether it's 10 out of 100, or 10 out of 10000. Asking 5000 people will give you the same pretty high accuracy regardless of whether it's 5000 of 1000 or 5000 of 1000000000.

5000 is a pretty high sample size by social science standards; it is definitely in the "as large as possible" category; and more precisely, in the "only governments and very large research institutions can afford to conduct an in-depth in-person survey of this scale at all" category.

"For example they lump drunk sex into rape" - no, as I already indicated above, the statistics on page 19 put it into a separate category.

The rest of your argument seems to boil down to saying "that just isn't true" about standard, ubiquitously used, basic, simple statistical tools, without any further explanation, and I'm afraid it is not an argument that can be taken seriously. This is really not advanced stuff, these formulas are one of the first things they teach in any introductory statistics class, e.g. in this class it's in week 4 "Inference for proportions".

Hi folks,

I'm working on an experiment by which I randomly assign n people to the treatment group (offered vouchers) and control group (offered nothing). My null hypothesis is offering vouchers will not impact the proportion of users who use the product in the experiment period.

For example, the hope is that 30% of the treatment group used the product while in contrast, only 10% of the control group used the product in the 2-weeks experiment period.

My question is, assuming everything else is constant, would changing the group size (n) alone is going to affect the final p-value?

I asked this because I'm taking a coursera class to refresh my stats knowledge, and it quotes:

>We are able to find the statistically
>
>significant result simply by inflating our sample size.
>
>And remember that the sample size is something
>
>the researcher has control over, because after all,
>
>you get to decide how many observations you want to sample.

In this case, the lecturer showed that she could make the non-stat-sig result to be stat-sig only by increasing the sample size, with everything else stays constant. Would this happen only on a survey scenario or also in the experimental scenario I asked above?

Thanks and please let me know if my question is not clear, English is not my first language.

No worries. I think DS Math Skills is ok if you want a review of very basic math topics, but there are better courses by Duke:

Introduction to Probability and Data Inferential Statistics Bayesian Statistics Linear Regression and Modeling

https://www.coursera.org/specializations/jhu-data-science

https://www.coursera.org/learn/probability-intro

https://www.coursera.org/specializations/executive-data-science

https://www.coursera.org/specializations/data-science-python

https://www.coursera.org/learn/inferential-statistics-intro

https://www.coursera.org/learn/basic-statistics

https://www.edx.org/course/statistics-and-r

https://www.edx.org/course/using-python-for-research

https://www.edx.org/course/python-for-data-science

https://www.edx.org/course/statistical-thinking-for-data-science-and-analytics

DataCamp.com

Yes, there are many free courses that you can use to learn the prerequisite mathematics. KhanAcademy would be my first recommendation, but you can also try some of these:

Inferential Statistics https://www.coursera.org/learn/inferential-statistics

Bayesian Statistics: From Concept to Data Analysis https://www.coursera.org/learn/bayesian-statistics

Inferential Statistics Intro https://www.coursera.org/learn/inferential-statistics-intro

Bayesian Statistics https://www.coursera.org/learn/bayesian

Basic Statistics https://www.coursera.org/learn/basic-statistics

Introduction to Probability https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2

Introduction to Linear Models and Matrix Algebra https://www.edx.org/course/introduction-linear-models-matrix-harvardx-ph525-2x-2

Intro to Descriptive Statistics https://www.udacity.com/course/intro-to-descriptive-statistics--ud827

Intro to Inferential Statistics https://www.udacity.com/course/intro-to-inferential-statistics--ud201

Mathematics for Computer Science https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/index.htm

An Intuitive Introduction to Probability https://www.coursera.org/learn/introductiontoprobability

Statistical Inference https://www.coursera.org/learn/statistical-inference

College Algebra and Problem Solving https://www.edx.org/course/college-algebra-problem-solving-asux-mat117x

Precalculus https://www.edx.org/course/precalculus-asux-mat170x

UT admissions sent an alternative list of courses, saying they can't give a definitive answer about the UCSD ones.

The courses they recommended are here:

STATISTICS

SDS 302: Fundamentals of Statistics, edX.org, MIT, 18 weeks: https://www.edx.org/course/fundamentals-of-statistics Statistics: Unlocking the World of Data, edX.org, Univ. of Edinburgh, 8 weeks https://www.edx.org/course/statistics-unlocking-the-world-of-data Basic Statistics: coursera.org, Univ. of Amsterdam, 8 weeks: https://www.coursera.org/learn/basic-statistic

Taken Together: 1) Introduction to Probability and Data with R, coursera.org, Duke Univ., 5 weeks https://www.coursera.org/learn/probability-intro 2) Inferential Statistics, coursera.org, Duke Univ., 5 weeks https://www.coursera.org/learn/inferential-statistics-intro

SDS 328M: BioStatistics: edX.org, Doane Univ., 8 weeks: https://www.edx.org/course/biostatistics-2

Taken Together: 1) Summary Statistics in Public Health, coursera.org, Johns Hopkins Univ., 4 weeks https://www.coursera.org/learn/summary-statistics 2) Hypothesis Testing in Public Health, coursera.org, Johns Hopkins Univ., 4 weeks https://www.coursera.org/learn/hypothesis-testing-public-health 3) Simple Regression Analysis in Public Health, coursera.org, Johns Hopkins Univ., 4 weeks https://www.coursera.org/learn/simple-regression-analysis-public-health

CALCULUS/MATH

Calculus 1A,B,C mitX, 13 weeks each https://www.edx.org/course/calculus-1a-differentiation Linear Algebra - Foundations to Frontiers, edX.org, Univ of Texas, 15 weeks https://www.edx.org/course/linear-algebra-foundations-to-frontiers The Math of Data Science: Linear Algebra, edX.org, RICE, 8 weeks https://www.edx.org/course/math-of-data-science-linear-algebra Gilbert Strang. RES.18-010 A 2020 Vision of Linear Algebra. Spring 2020. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.