The data collected does not have to be measurable.
In continuous grouped data the data is collected continuously and in groups. Data collected is in class intervals the actual data values are not visible.
Sampling errors are errors in the data collected during the carrying out of quantitative data surveys. They can occur for various reasons, e.g. surveys that were incorrectly filled out. It is generally said that a survey needs to have a margin of error of under 3% to be statistically significant.
how is data collected and used for the purpose of national statistics
Primary data is the the data that collected by yourself. While secondary data is those collected by others and to be reused by yourself.
data can be collected many different ways, but a survey can be cunducted in a few different ways some of them are: simple random, stratified, block samples stratified simple random
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Factors such as instrument precision, human error, environmental conditions, and random variations in the system can all contribute to measurement error in an experiment. It is important to account for these factors and take measures to minimize their impact in order to ensure the accuracy and reliability of the data collected.
Possible error
Maximum Random Error is often calculated by subtracting the average from the data point farthest from the average.
Any kind of data can be collected.
The data collected does not have to be measurable.
Data can be collected for independent samples by randomly selecting individual units or cases from the population of interest. This can be done using random sampling techniques such as simple random sampling, stratified sampling, or cluster sampling. By ensuring that each sample is selected independently of the others, we can maintain the assumption of independence among the samples in the data analysis.
Data that is collected may have been collected previously for some reason, or it might have been collected recently. Data is usually collected to show statistics or information about something specific.
Errors in experiments can be corrected by identifying the source of the error, such as equipment malfunction or human error, and then implementing corrective actions. This can involve recalibrating equipment, double-checking procedures, or repeating the experiment to confirm results. It's important to document any errors and their corrections to ensure the reliability of the experimental data.
The polarity of a random error refers to whether the error is positive or negative relative to the true value. In statistical analysis, random errors can be equally likely to be positive or negative, and their effect should cancel out when many measurements are averaged. Monitoring polarity can help identify biases or systematic errors in data collection or measurement processes.
The collected data is organized in a fashion so you can determine if the hypothesis is supported.