Question

What happens when a dataset includes records with missing data?

Answer 1

Answer:

The missing data adds ambiguity to the data. It is represented as NA or  NAN. If the dataset is small then every data point counts. The missing data creates imbalance in the observations and can even lead to invalid conclusions. A company could suffer great loss because, small or big, any type of data is very important. So data must be kept safe.

The most effective way to deal with them is by minimizing the occurrence of missing data at production. To deal with missing data, Data Scientists compare two datasets , one with the missing observations and one without them. Using a t-test if there is no difference between the two datasets then the missing data wouldn't affect the results.

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Answer 2

• The missing data adds to the data's ambiguity. It is denoted by the letters NA or NAN.
• When the dataset is small, each data point is significant.
• The absence of data causes an imbalance in the observations, which might lead to incorrect conclusions.
• Because any form of data, no matter how tiny or large, is extremely valuable, a firm could incur a significant loss.
• As a result, data must be kept secure.
• The most efficient strategy to deal with them is to reduce the number of times missing data occurs in production.
• To deal with missing data, Data Scientists compare two datasets, one with and one without the missing observations.
• If there is no difference between the two datasets, the missing data will not affect the results when using a t-test.

How does missing data affect results?

• Missing data present various problems.
• First, the absence of data reduces statistical power, which refers to the probability that the test will reject the null hypothesis when it is false.
• Second, the lost data can cause bias in the estimation of parameters.
• Third, it can reduce the representativeness of the samples.

What to do when data is missing?

• Use deletion methods to eliminate missing data.
• The deletion methods only work for certain datasets where participants have missing fields.

• Use regression analysis to systematically eliminate data.
• Data scientists can use data imputation techniques.

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