Repeated trials are where you measure the same thing multiple times to make your data more reliable. This is necessary because in the real world, data tends to vary and nothing is perfect. The more trials you take, the closer your average will get to the true value.

Replication is an essential process because, whenever a cell divides, the two new daughter cells must contain the same genetic information, or DNA, as the parent cell. Once the DNA in a cell is replicated, the cell can divide into two cells, each of which has an identical copy of the original DNA.

When we do experiments it’s a good idea to do multiple trials, that is, do the same experiment lots of times. When we do multiple trials of the same experiment, we can make sure that our results are consistent and not altered by random events. Multiple trials can be done at one time.

Accuracy is a measure of how your measured value compares with the real value. This number is more precise than their previous result since the uncertainty is smaller, but the new measured value of mass is very different from their previous value.

Test score reliability is a component of validity. If test scores are not reliable, they cannot be valid since they will not provide a good estimate of the ability or trait that the test intends to measure. Reliability is therefore a necessary but not sufficient condition for validity.

Improve the reliability of single measurements and/or increase the number of repetitions of each measurement and use averaging e.g. line of best fit. Repeat single measurements and look at difference in values. Repeat entire experiment and look at difference in final results.

1. 5 Ways to Improve Equipment Reliability and Avoid Downtime in Your Food Processing Plant. Jun 22, 2017 Views: 22405 No Comments.
2. Train plant employees.
3. Use high-quality lubricants.
4. Invest in equipment redundancy.
5. Conduct consistent cleaning and maintenance.
6. Use automation solutions.

You can increase the validity of an experiment by controlling more variables, improving measurement technique, increasing randomization to reduce sample bias, blinding the experiment, and adding control or placebo groups.