library(swirl)##
## | Hi! Type swirl() when you are ready to begin.swirl()Choose Statistical Inference
| Please choose a lesson, or type 0 to return to course menu.
1: Introduction 2: Probability1 3: Probability2 4: ConditionalProbability 5: Expectations
6: Variance 7: CommonDistros 8: Asymptotics 9: T Confidence Intervals 10: Hypothesis Testing
11: P Values 12: Power 13: Multiple Testing 14: Resampling
Selection: 1| Introduction to Statistical_Inference. (Slides for this and other Data Science courses may be found at github
| https://github.com/DataScienceSpecialization/courses. If you care to use them, they must be downloaded as a zip file and viewed locally.
| This lesson corresponds to Statistical_Inference/Introduction.)
| In this lesson, we'll briefly introduce basics of statistical inference, the process of drawing conclusions "about a population using noisy statistical data where uncertainty must be accounted for". In other words, statistical inference lets scientists formulate conclusions from data and quantify the uncertainty arising from using incomplete data.
| Which of the following is NOT an example of statistical inference?
1: Constructing a medical image from fMRI data
2: Testing the efficacy of a new drug
3: Polling before an election to predict its outcome
4: Recording the results of a statistics exam
Selection: 4
| So statistical inference involves formulating conclusions using data AND quantifying the uncertainty associated with those conclusions. The uncertainty could arise from incomplete or bad data.
| Which of the following would NOT be a source of bad data?
1: Selection bias
2: A poorly designed study
3: A randomly selected sample of population
4: Small sample size
Selection: 3
So with statistical inference we use data to draw general conclusions about a population.
Which of the following would a scientist using
| statistical inference techniques consider a problem?
1: Our study has no bias and is well-designed
2: Our data sample is representative of the population
3: Contaminated data
Selection: 3
| Nice work!
| Which of the following is NOT an example of statistical inference in action?
1: Determining a causative mechanism underlying a disease
2: Counting sheep
3: Estimating the proportion of people who will vote for a candidate
4: Testing the effectiveness of a medical treatment
Selection: 2
| You got it right!
| We want to emphasize a couple of important points here. First, a statistic (singular) is a number computed from a sample of data. We use
| statistics to infer information about a population. Second, a random variable is an outcome from an experiment. Deterministic processes,
| such as computing means or variances, applied to random variables, produce additional random variables which have their own distributions. It's important to keep straight which distributions you're talking about.
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| Finally, there are two broad flavors of inference. The first is frequency, which uses "long run proportion of times an event occurs in
| independent, identically distributed repetitions." The second is Bayesian in which the probability estimate for a hypothesis is updated as additional evidence is acquired. Both flavors require an understanding of probability so that's what the next lessons will cover.
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| Congrats! You've concluded this brief introduction to statistical inference.