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no matter what type of population value (or combination of population values) is being estimated using a confidence interval, what items should you be watching for in order to best assess the results?

Sagot :

1. The actual differences that researchers imply causality come from randomized experiments.

2.  That the sample selected represents the population.

3. Confidence level clearly stated.

Population values, also called parameters, are calculated from all values ​​in the entire population.

A representative sample is a subset of the population that attempts to accurately reflect the characteristics of the larger group. For example, in a classroom of her 30 students, 15 boys and 15 girls, you can create a representative sample of her 6 students, 3 boys and 3 girls.

Probable sampling is defined as a sampling technique in which the researcher selects samples based on the researcher's subjective judgment rather than at random. This is a less rigorous method. This sampling method relies heavily on the researcher's expertise. This is done through observation and is often used by researchers for qualitative research.

A confidence interval (CI) is the range of estimated values ​​for an unknown parameter. Confidence intervals are computed at the specified confidence level. A 95% confidence level is the most common, but other levels such as 90% and 99% are sometimes used. The confidence level represents the proportion of corresponding CIs over time that contain the true value of the parameter. For example, 95% of all intervals computed at the 95% level must contain the true value of the parameter.

Factors that affect the width of the CI include confidence level, sample size, and intra-sample variation. All other things being equal, the larger the sample, the narrower the confidence interval. Similarly, the greater the sample variability, the wider the confidence interval, and the higher the confidence level, the wider the confidence interval should be.

Learn more about Confidence Interval:

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