M205 > Lesson 10 > Day 2 by Daniel Chua

TEAM 1

200444541 — 2021-07-27 02:32:03 UTC

If the sample design is not well done, it could affect the statistical significance of the data collected as the p-value would be higher, which is not what we want, we usually try to aim for a lower p-value. Hence, the sample design needs to reflect the characteristics of the population as accurately as possible to reduce error. Furthermore, the data collected would be void as it would be not reflecting reliable and accurate data. Therefore, a poor sample design can lead to wrong or misleading findings of results that might not support the overall justifications of the conclusion.

Team 2

20014291 — 2021-07-27 02:55:51 UTC

Data in the form of statistics for samples greatly affect the significance since the data should be representing the population of the desired sampling design.
It is important to consider which kind of sampling design is appropriate for the whole outcome of the research.
The sampling design affects statistical significance. The sampling design determines the sample we’re selecting from a whole population and it has to be representative of the population as well. The sample group and size affects the accuracy of whether the final results of the statistic accurately represents the population.
Statistical significance calculates the probability of results using p-value. To ensure the results have a chance of little to no errors, the p-value should be low, and having appropriate sampling design is important to ensure the p-value is low.
Sample design brings and represents the numbers of the population, thus to ensure almost accuracy and a lower p-value, sampling has to be suitable to counter the population since the data collected would greatly affect the statistical significance in data.

Team 3

200028181 — 2021-07-27 04:05:10 UTC

Sampling design can adversely affect the statistical significance in the data we acquire. We ideally want to achieve a low P value, which means to have a higher chance of confidence that our data characteristic actually represents the population. Choosing a good sampling design can help us in this aspect. The sampling design is important in helping to direct us to the representative sample population, along with the sample group and size. The sample population that we chose to conduct our research on would directly affect how accurate our final results and findings are in representing the whole population. Having a good sampling design eliminates bias, increases the credibility of your report and in turn, increases statistical significance as having our sample data exhibit these traits leads to a lower P value overall. Thus, leading to a greater confidence of the data characteristics being representative of the whole population and ensuring the data is accurate.

Team 4

2021-07-27 07:01:48 UTC

Statistics for sample design can greatly affect statistical significance because if there is an error or questions that are not logical, the sample design would not reflect the correct characteristics of the data. This will eventually pull down our confidence and increase in the p-value that represents the population characteristic. The increase in p-value would suggest that the data results are ineffective. Our sample design must consist of the correct data because the slightest difference can affect our conclusions and the overall statistical significance. Our p value must be <0.05 or lower in order to be credible. If we want higher confidence in our data, we should set the p-value lower than 0.01/0.001. Thus, when choosing the sampling design, we must ensure that the design chosen is appropriate due to the different characteristics in different populations as this would lead to low p-value as we know that errors will be reduced due to the appropriate design chosen.