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What else can I help you with?
Why was it important for mendel to study such a large sample of pea plant?
(Apex Learning) A higher sample size gives more accurate results.
Disadvantages of a large sample size confidence Interval in statistices?
A disadvantage to a large sample size can skew the numbers. It is better to have sample sizes that are appropriate based on the data.
Why was it important for Mendel such a large sample of peas plants to determined the probability of inheritance?
Answer D- A higher sample size gives more accurate results- APEX LEARNING
Why was it important for Mendel to study such a large sample pea plants to determine the probability of inheritance?
Answer D- A higher sample size gives more accurate results- APEX LEARNING
Why was it important to Mendel to study such a large sample of pea plants to determine the probability of inheritance?
Answer D- A higher sample size gives more accurate results- APEX LEARNING
When the sample size is large valid confidence intervals can be established for the population mean irrespective of the shape of the underlying distribution?
Yes, but that begs the question: how large should the sample size be?
What sample size is sufficient for stat?
A sample size of one is sufficient to enable you to calculate a statistic.The sample size required for a "good" statistical estimate will depend on the variability of the characteristic being studied as well as the accuracy required in the result. A rare characteristic will require a large sample. A high degree of accuracy will also require a large sample.
Will a large sample size and a small sample variance produce the largest value for the estimated standard error?
no
What is the benefit of using a large sample size in an experiment?
Statistically the larger the sample size the more significant the results of the experiment are. Chance variation is ruled out.
What does it means if the standard deviation is large?
that you have a large variance in the population and/or your sample size is too small
Why do you use a large sample size when conducting an experiment?
Better the results
What does the Central Limit Theorem say about the traditional sample size that separates a large sample size from a small sample size?
The Central Limit Theorem states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. This fact holds especially true for sample sizes over 30.
