It made his actual results approach the results predicted by probability
The sample size has no effect on the validity of an experiment: instead, it is the experimental procedure and integrity of the experimenters.The sample size can affect conclusions that may be drawn from an experiment. The larger the sample is, the more reliable these conclusions are.
Generally, the larger the sample the more reliable the results. Example: If you flipped a coin twice and got heads both times you could say the coined is biased towards heads. However, if you repeat the experiment 100 times your results will be a lot more reliable.
Convenience sample Systematic sample Simple random sample (SRS) Census
The bigger the sample size the more accurate the results will be. For example, if you roll a 6 sided die and track the results to get the probability of rolling a six. If you only roll 6 times, then you may not even get one 6 or you could get a few. A small sample size means you won't get very reliable results.
The sample should be selected randomly.
An experimental sample is an experiment that is just a sample of what you are looking for.
Drawing a conclusion based on too small a population sample is not reliable because the sample may not accurately represent the entire population, leading to biased or inaccurate results. It is important to use a sufficiently large and diverse sample size to ensure the validity and generalizability of conclusions.
Heating a wet sample to constant mass is necessary in laboratory analysis to ensure accurate and reliable results because it removes any remaining moisture or volatile substances that could affect the measurements. This process helps to eliminate errors and ensures that the sample's weight is consistent, allowing for precise analysis and reliable data.
A well-designed experiment with a large sample size and controlled variables typically produces the most reliable results. Additionally, experiments that are repeated multiple times to account for variability and ensure consistency tend to yield reliable outcomes.
it made his actual results approach the results predicted by probability.
Standard curves are necessary in experiments to accurately quantify the concentration of a target substance in a sample. By comparing the signal generated from the sample to the standard curve, researchers can determine the exact concentration of the substance. This helps ensure the accuracy and reliability of the experimental results.
The sample size has no effect on the validity of an experiment: instead, it is the experimental procedure and integrity of the experimenters.The sample size can affect conclusions that may be drawn from an experiment. The larger the sample is, the more reliable these conclusions are.
A control sample is the experiment under regular conditions. An experimental sample is the experiment in which different variables are changed.
Generally, the larger the sample the more reliable the results. Example: If you flipped a coin twice and got heads both times you could say the coined is biased towards heads. However, if you repeat the experiment 100 times your results will be a lot more reliable.
Convenience sample Systematic sample Simple random sample (SRS) Census
Accurate results can be difficult to obtain during experimentation due to various factors, such as human error, equipment limitations, environmental influences, and variability in the sample being tested. It is essential to carefully control these variables, use proper experimental techniques, and replicate the study to ensure the accuracy and reliability of the results.
A control sample or control group is used to compare with the experimental group or sample. The control sample ideally, should be exactly the same as the experimental sample except that you don't give your experimental treatment to the control sample. Afterwards you compare the 2 samples to see if your experimental treatment had any kind of effect. The control is like a reference point.