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# When your sample data is all negative values how can you convert it for use on a normal distribution?

Updated: 10/19/2022

Wiki User

10y ago

The data from a normal distribution are symmetric about its mean, not about zero. There is, therefore nothing strange about all the values being negative.

Wiki User

10y ago

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Q: When your sample data is all negative values how can you convert it for use on a normal distribution?
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### The distribution of sample means is not always a normal distribution Under what circumstances will the distribution of sample means not be normal?

The distribution of sample means will not be normal if the number of samples does not reach 30.

### What is the expected shape of the distribution of the sample mean?

The distribution of the sample mean is bell-shaped or is a normal distribution.

### Is it possible for sample not normal to be from normal population?

Yes. You could have a biased sample. Its distribution would not necessarily match the distribution of the parent population.

### What are characteristics of the X2?

It is not negative. it is positively skewed, and it approaches a normal distribution as the degrees of freedom increase. Its shape is NEVER based on the sample size.

### Can one treat sample means as a normal distribution?

Not necessarily. It needs to be a random sample from independent identically distributed variables. Although that requirement can be relaxed, the result will be that the sample means will diverge from the Normal distribution.

### What distribution does the F distribution approach as the sample size increases?

The F distribution is used to test whether two population variances are the same. The sampled populations must follow the normal distribution. Therefore, as the sample size increases, the F distribution approaches the normal distribution.

### What happens to the distribution of the t-score as the sample size increases?

It approaches a normal distribution.

### Why the normal distribution can be used as an approximation to the binomial distribution?

The central limit theorem basically states that for any distribution, the distribution of the sample means approaches a normal distribution as the sample size gets larger and larger. This allows us to use the normal distribution as an approximation to binomial, as long as the number of trials times the probability of success is greater than or equal to 5 and if you use the normal distribution as an approximation, you apply the continuity correction factor.

### Is the distribution of sample means always a normal distribution If not why?

It need not be if: the number of samples is small; the elements within each sample, and the samples themselves are not selected independently.

### What is the mean of the sampling distribution of the sample mean?

Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the &quot;normal distribution.&quot; When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.

### What is the sampling distribution of sample means and why is it useful?

Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the &quot;normal distribution.&quot; When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.

### What does when the sample size and degrees of freedom is sufficiently large the difference between a t distribution and the normal distribution becomes negligible mean?

The t-distribution and the normal distribution are not exactly the same. The t-distribution is approximately normal, but since the sample size is so small, it is not exact. But n increases (sample size), degrees of freedom also increase (remember, df = n - 1) and the distribution of t becomes closer and closer to a normal distribution. Check out this picture for a visual explanation: http://www.uwsp.edu/PSYCH/stat/10/Image87.gif