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So you have a population of all people, and four samples drawn from that population, called red, brown, blonde, and black. Since this is a problem where sample sizes differ, we should use a z-score that takes sample size into account. Normal z score = (observation - average)/(standard...

I hypothesize attitudes toward justice correlate with annual wages. Does the survey taker earn more than the median for 1 person? More than ten people? Less than 1/5th?

80/100 = 4/5 of answers are true, 1/5 of answers are false. If the subject knows this, then their best guessing strategy is to guess true 4/5 of the time and false 1/5 of the time.

Gambler's Fallacy

The Gambler's Fallacy is a trick of memory, where the observer considers what has been observed but doesn't remember what hasn't taken place.

Mean for the rate in ml/s wasn't calculated in the original, so I madea copy and added it (blue outlined cells) I also agree the time to complete the fill operation is a better measure. The average speed in ml/sec varies considerably between pumps, and so a small variance in the smallest...

Let's say I'm a test subject, and I recall 15 words. That information alone isn't very informative until we also include the fact there are 50 words. Then we have a proportion: 15 out of 50 words = 15/50 = 3/10 = 30% of the total words.

Confidence Level: 90% Margin of Error: 5% According to this sample size calculator, the ideal sample size is 271 for very large population, the first answer listed in post #4

Standard error solves problems that standard deviation alone can't. Standard deviation -- average distance away from average -- is good for some problems, but doesn't have anything to say about sample size. Whereas standard error takes into account the sample size; it's still standard...

The problem you're referring to is standard error = (standard deviation) / Sqrt(sample size), used for z-score = (observation - mean)/(standard error) that measures how outstanding an individual is relative to the sample average. As the sample size goes up the standard error goes down, and so...

Example data, with polynomial trend line for x starting attitude = x^0 = 0.875 velocity = x^1 = 0.196x rate of change (acceleration) = x^2 = 0.161x^2

So it's a measurement that can change over time, and you're interested in the rate of change, aka acceleration. enjoyment / sessionTime^0 = location enjoyment / sessionTime^1 = velocity enjoyment / sessionTime^2 = acceleration

The data given isn't normally distributed It's skewed by 0.5395 and so calculations on the area beneath the curve will have to take that into account.

yes, the two standard deviations are part of the denominator. The MATH thing doesn't seem to be translating LaTex.

Statistics Knowledge Portal corellation(x,y) = covariance(x,y) / standard deviation(x) * standard deviation(y)

correlation and p values

There are four possible outcomes to a hypothesis test

That's a decision threshold you must choose, typically selected as 0.05 For example:

Two hypotheses, stated as the opinion hypothesis vs it's alternative: H1: There is a positive correlation between FDS and SAS H0: There isn't a positive correlation between FDS and SAS H1: There isn't a correlation between FDS and STAI H0: There is a correlation between FDS and STAI The p...